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REVIEW 4 major objections 5 minor 258 references

A review on Machine Learning based User-Centric Multimedia Streaming Techniques

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A survey that maps machine-learning QoE prediction and adaptive streaming for 2D and 360-degree video, and argues that continuous, time-varying QoE modeling should drive user-centric streaming decisions.

desk verdict A broad but uneven survey of ML-based QoE and streaming; the tables are handy but the lack of methodology and self-citation bias keep it from being the go-to reference. read the letter →

arxiv 2411.15801 v1 pith:KFQ4SEVO submitted 2024-11-24 cs.MM cs.AIcs.LG

Monalisa Ghosh , Chetna Singhal This is my paper · ORCID
classification cs.MMcs.AIcs.LG
keywords QualityofExperienceQoEpredictionadaptivevideostreamingmachinelearning360-degreeviewportreinforcementassessment
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This survey paper sets out to organize the fast-growing body of research on machine-learning-based Quality-of-Experience (QoE) prediction and adaptive streaming for both conventional and 360-degree video. Its central claim is that QoE should be treated not as a one-time overall score but as a continuous, time-varying quantity that can be learned from objective video metrics, impairment events such as rebuffering, and user behavior, and then used to drive bit-rate and tile-selection decisions in real time. The paper argues that a user-centric paradigm, where the client actively adapts based on predicted perceptual quality rather than just network throughput, is the natural direction for future multimedia streaming systems. It matters because streaming video dominates internet traffic, and the proposed ML-based approaches promise to improve viewer experience while making more efficient use of bandwidth. The survey's value lies in its synthesis: it brings together QoE definitions, assessment methodologies, ML-based QoE prediction models, adaptive streaming algorithms, and publicly available datasets under one coherent framework.

What carries the argument

The central organizing object is the ML-based QoE prediction model that maps input features—objective VQA metrics (PSNR, SSIM, MS-SSIM, VMAF, etc.), impairment factors (stalling, rebuffering, quality switches), and user-centric signals (viewport, head movement, gaze)—to a predicted QoE score, either overall or frame-by-frame. For continuous QoE, the key mechanism is temporal modeling with recurrent or convolutional architectures (LSTM, bidirectional LSTM, temporal convolutional networks) that capture the hysteresis effect and long-term dependencies in perceived quality. For adaptive streaming, the central mechanism is the bit-rate adaptation (ABR) algorithm, increasingly formulated as a reinforcement learning problem where an agent selects bit-rates or tile qualities to maximize QoE. For 360-degree video, the additional key mechanism is viewport prediction (using head movement traces, saliency maps, and eye tracking) combined with tile-based streaming, where only the tiles in the predicted viewport are streamed at high quality.

What would settle it

A systematic re-review of the same literature using a transparent search and selection protocol (e.g., strict inclusion criteria, PRISMA-style flow diagram, multiple independent coders) that a year later produces a materially different landscape of what counts as the leading ML-based QoE/streaming techniques would show whether the survey's coverage is representative. Alternatively, reproducing the Figure 7 analysis with a different head-orientation dataset and showing that the tile popularity distribution is substantially different would cast doubt on the implied motivating observation for viewport-adaptive streaming.

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Extended reading notes

Core claim

The paper claims that the state of the art in multimedia streaming is converging on a user-centric, machine-learning-driven approach in which QoE is modeled as a continuous, time-varying signal rather than a single overall score. It argues that ML models—especially recurrent architectures like LSTM and attention-based models—can capture the temporal dependencies, hysteresis effects, and non-linear interactions between video quality, rebuffering, and bit-rate switches that traditional parametric QoE models miss. For 360-degree video, the paper contends that viewport-adaptive, tile-based streaming driven by ML-based viewport prediction is the key to reducing bandwidth requirements while maintaining immersion. The survey catalogs a wide range of ML-based QoE prediction models (e.g., ATLAS, DEMI, DeSVQ, MO-QoE, NARX, LSTM-based models, bidirectional LSTM, temporal convolutional networks) and adaptive streaming techniques (e.g., Pensieve, Comyco, SAC-ABR, ABRaider, D-DASH, FReD-ViQ, and 360-degree-specific systems like DRL360, PARSEC, NOVA, RoSal360), showing that the field has moved from heuristic buffer- and throughput-based adaptation to learning-based policies that optimize perceptual quality directly.

Load-bearing premise

The survey's accuracy and usefulness depend on the assumption that the authors' selection and summaries of the cited literature accurately reflect the state of the art, and that the small empirical analysis of tile viewing patterns generalizes beyond the specific dataset and 360-degree video used.

Editorial extensions

If this is right

  • If ML-based continuous QoE prediction becomes reliable enough, streaming clients can make bit-rate decisions that optimize perceived quality over time, rather than just minimizing rebuffering or maximizing throughput.
  • For 360-degree video, accurate ML-based viewport prediction combined with tile-based adaptive streaming could substantially reduce bandwidth requirements while maintaining or improving immersive quality.
  • The availability of public datasets (subjective scores, network traces, head movement, eye tracking) could enable standardized benchmarking and reproducible comparison of QoE prediction models and streaming policies.
  • Integration of ML-based QoE prediction with ABR algorithms could shift the field from network-centric to truly user-centric streaming, where each user's experience is individually optimized.
  • The trend toward deep learning models for QoE prediction may continue, with hybrid approaches (e.g., CNN+LSTM, fuzzy logic+RL) addressing both accuracy and computational efficiency.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • One implicit consequence of the survey's framing is that QoE prediction should be evaluated not only by correlation with subjective scores, but by its downstream impact on adaptation decisions; a model that predicts QoE well on offline datasets may still fail when used in a closed-loop streaming system, since the adaptation changes what the user sees.
  • The survey's emphasis on datasets suggests a testable extension: a standardized, continuously updated benchmark that combines QoE prediction accuracy with streaming performance metrics (rebuffering, quality switches, bandwidth efficiency) across diverse network traces and content types would be a natural next step for the field.
  • The paper's treatment of 360-degree streaming implies that viewport prediction accuracy is the single most load-bearing component for bandwidth savings; yet most systems still rely on historical head movement data, and the open challenge of predicting viewport under volatile head motion suggests that robust uncertainty-aware prediction could be a more fruitful direction than chasing marginal accur
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. This manuscript is a survey of machine-learning-based QoE modeling and user-centric adaptive streaming for conventional and 360-degree video. It covers subjective and objective quality assessment, overall and continuous time-varying QoE prediction, adaptive bit-rate streaming, tile-based 360-degree streaming, public datasets, and open research challenges. The paper positions itself as a comprehensive overview, and it includes comparative tables (Tables 5, 7, and 8) with reported performance gains, a short head-orientation analysis in Section 3.2, and an inventory of datasets in Tables 9-11.

Significance. If the synthesis is reliable, this survey would be a useful entry point for researchers, especially because it combines QoE modeling and ML-based streaming for both conventional and 360-degree video in one place. The dataset inventory in Tables 9-11 and the list of open challenges are practically valuable. The paper also makes a clear organizational effort, separating overall from continuous QoE prediction and conventional from immersive streaming. However, the reference value of the survey depends on the accuracy and representativeness of the literature synthesis; the absence of a documented selection methodology and the untraceable quantitative entries in the comparison tables currently weaken that value.

major comments (4)
  1. [Section 1.1 (general methodology)] The paper claims to provide 'a comprehensive overview' but never describes a systematic search strategy, inclusion/exclusion criteria, or a data extraction protocol. For a survey whose central claim is coverage and synthesis, this omission is load-bearing. The authors should either add a methodology subsection (e.g., databases searched, search strings, time window, screening criteria) or explicitly temper the comprehensiveness claim.
  2. [Tables 5, 7, and 8] The Accuracy columns report concrete numerical gains without traceable provenance. For example, Table 5 lists DeSVQ as '4.8-20% relative PLCC gain', Table 7 lists MAIVS as '8.52%, 48.18% lowered bit-rate', and Table 8 lists the [214] row as '33-71% relative LCC increase'; none of these entries states the baseline, dataset split, or metric definition used to compute them. Please add a mapping from each table entry to the exact figure, table, or section of the cited paper, or remove the overly precise values. This traceability is necessary for the survey to serve as a reliable reference.
  3. [Section 3.2, Figure 7] The small empirical study of 55 viewings is not reproducible: the text does not specify which video content from datasets [64, 65, 23] was used, the tile grid geometry, the projection format, the QP encoding configuration, or the algorithm that maps head orientation to tile numbers. In addition, Figure 7(a) shows 61 tiles on the x-axis but the text states that tiles 57-64 are least viewed. Please provide the processing details or remove the analysis; as written, it does not substantiate the claim that FoV-based bit-rate adaptation is motivated by these statistics.
  4. [Tables 5-8 and Section 5.1] The authors' own prior work appears frequently in the central comparison tables and is consistently presented as best-performing (e.g., MO-QoE in Table 5 and Figures 11-12, M-3R and DeSVQ in Table 5, MAIVS in Tables 7-8, and I2MB in Section 2.2). Self-citation is not improper, but without inclusion criteria the prominence of these works raises a selection-bias concern that should be addressed explicitly. The authors should state how all entries were chosen for the tables and whether any independent re-evaluation or replication of the numbers was performed.
minor comments (5)
  1. [Section 2.1, Table 2] There is a typo in the text: 'WCPPPSNR' should be 'WCP-PSNR'. Additionally, several equations in Table 2 contain garbled subscripts and notation (e.g., NC-PSNR and WS-SSIM entries) that are hard to parse; please clean up the LaTeX.
  2. [Section 2.3, Table 3] The SROCC values in Table 3 (e.g., PSNR 0.3527, SSIM 0.5119) are presented without a citation or a description of the computation. Please verify these numbers against the cited source and specify the exact database version and metric configuration.
  3. [Section 4.2] There is a typo: 'It is uded to measure' should be 'It is used to measure'.
  4. [Figure 4] Figure 4 appears to contain original analysis, but the caption cites [48, 49, 50] ambiguously. Please clarify whether the correlation curves are reproduced from those papers or computed by the authors, and describe the video samples and preprocessing.
  5. [Table 9] The rating type 'overall+cont.' in Table 9 is not explained in the caption; please expand the abbreviation to 'overall and continuous' or define it in the table notes.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation found: this survey summarizes prior work and contains no derivation chain that reduces to its own inputs.

full rationale

This manuscript is a literature survey, not a derivation. Its central claim is comprehensive coverage of ML-based QoE modeling and adaptive streaming, which depends on literature selection and faithful summarization rather than on a chain of equations or fitted parameters. The quantitative entries in Tables 5, 7, and 8 (e.g., '4.8-20% relative PLCC gain' and '8.52%, 48.18% lowered bit-rate') are attributed to cited external works, including the authors' own prior papers such as DeSVQ [51] and MAIVS [37]. Presenting one's own prior results inside a survey is self-citation, but it is not load-bearing circularity here: no argument in the paper derives a new prediction from those tables, and no self-cited theorem is invoked to forbid alternatives or force a choice. Section 3.2's Figure 7 is a descriptive empirical illustration of head orientations and tile bitrates/PSNR values, not a fitted model whose output is later relabeled as a prediction. The loss functions, metrics, and k-fold cross-validation formulas in Section 4 are standard tools introduced for context, not used to derive the survey's coverage claim. The absence of a systematic search protocol or traceable data-extraction rules weakens the comprehensiveness claim as a methodological matter, but that is a correctness or reproducibility risk, not circular reasoning. No specific reduction of the form 'Eq. X equals Eq. Y by construction' or 'fitted parameter renamed as prediction' can be exhibited, so the appropriate finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

As a review, the paper introduces no free parameters or invented entities. The central claim of comprehensiveness rests on domain assumptions about the validity of the QoE taxonomy and the accuracy of secondary summaries.

assumptions (2)
  • domain assumption The taxonomy of QoE factors (system, human, content, context) is a valid standard.
    The paper builds its discussion on this taxonomy (Section 2.1, Fig. 1) without justifying it or considering alternative frameworks.
  • domain assumption The descriptions of cited methods are accurate reflections of the original papers.
    The survey's value depends on faithful summarization, but no verification process is described.

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Cite this review

Pith. "Pith review of A review on Machine Learning based User-Centric Multimedia Streaming Techniques." pith.science (2026). https://pith.science/paper/KFQ4SEVO

@misc{pith2026241115801,
  author       = {Pith},
  title        = {Pith review of: A review on Machine Learning based User-Centric Multimedia Streaming Techniques},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KFQ4SEVO}},
  note         = {Machine review of arXiv:2411.15801}
}
abstract

The multimedia content and streaming are a major means of information exchange in the modern era and there is an increasing demand for such services. This coupled with the advancement of future wireless networks B5G/6G and the proliferation of intelligent handheld mobile devices, has facilitated the availability of multimedia content to heterogeneous mobile users. Apart from the conventional video, the 360$^o$ videos have gained popularity with the emerging virtual reality applications. All formats of videos (conventional and 360$^o$) undergo processing, compression, and transmission across dynamic wireless channels with restricted bandwidth to facilitate the streaming services. This causes video impairments, leading to quality degradation and poses challenges in delivering good Quality-of-Experience (QoE) to the viewers. The QoE is a prominent subjective quality measure to assess multimedia services. This requires end-to-end QoE evaluation. Efficient multimedia streaming techniques can improve the service quality while dealing with dynamic network and end-user challenges. A paradigm shift in user-centric multimedia services is envisioned with a focus on Machine Learning (ML) based QoE modeling and streaming strategies. This survey paper presents a comprehensive overview of the overall and continuous, time varying QoE modeling for the purpose of QoE management in multimedia services. It also examines the recent research on intelligent and adaptive multimedia streaming strategies, with a special emphasis on ML based techniques for video (conventional and 360$^o$) streaming. This paper discusses the overall and continuous QoE modeling to optimize the end-user viewing experience, efficient video streaming with a focus on user-centric strategies, associated datasets for modeling and streaming, along with existing shortcoming and open challenges.

Figures

Figures reproduced from arXiv: 2411.15801 by the authors.

Figure 1
Figure 1. Factors influencing QoE According to the European Union (EU) Qualinet Community [4], QoE is defined as “the degree of delight or annoyance of the user of an application or service. It results from the fulfillment of his/her expectations with respect to the utility and/enjoyment of the application or service in the light of the user’s personality and current state”. As per International Telecommunications Union (ITU)… view at source ↗
Figure 2
Figure 2. Categorization of VQA methodologies 4 [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Video transmission over wireless access network As videos pass through multiple processing stages before reaching the end-users, the effect of most of them is to degrade the video quality. Videos can have significant distortions at several stages, such as during processing (image acquisition), compression (encoding), or transmission. At most of the stages, video quality deteriorates [PITH_FULL_IMAGE:figures/full_fi… view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: (a) Correlation between QoE of current and past frame indices for the given video samples (b) Video samples (from left to right) (i) v1 (ii) v2 (iii) v3 (iv) v4 (v) v5 (vi) v6 of our article with respect to existing surveys in literature. In this survey we holistically…
Figure 5
Figure 5. Figure 5: Adaptive video streaming framework DASH is an over-the-top wireless streaming technology that is prominent and effectively enables the adaptability of content delivery in response to changing network conditions [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]
Figure 6
Figure 6. Figure 6: (a) Head navigation direction parameters (b) User’s viewport (c) Equirectangular projection. 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 Tile number 0 20 40 60 Number of users (a) 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 Tile number 0 200 400 Bit rate [kbps] QP=15…
Figure 7
Figure 7. Figure 7: (a) Number of users (b) Bit-rate (c) PSNR of the 360◦ video tiles. 360◦ videos [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]
Figure 8
Figure 8. Figure 8: Outline of a ML based QoE predictor past and their fluctuating psychological states and emotional conditions during each playback. Users might adhere to entirely different content depending on moods and mental state. Therefore, it is crucial to develop effective strate…
Figure 9
Figure 9. Figure 9: Generalized framework for user-centric multimedia streaming technological improvements and the significant network bandwidth requirements of streaming users, the primary obstacle in content delivery lies in the development of network-aware solutions aimed at enhancing …
Figure 10
Figure 10. Figure 10: (a), (b) Variation of different input features vs frame index for the distorted video sample Mask from [48] (c) Variation of subjective MOS vs frame index and indication of re-buffering events (d) Snapshot of the video sample Mask elements of practical systems by inte…
Figure 11
Figure 11. Figure 11: Scatter plot depicting the correlation between actual and predicted video quality scores for the most effective MO-QoE model for different test ((a) LIVE [31] (b) VQEG HD3 [117] (c) Waterloo [118]) datasets. Source[14] VQA measures are combined within the groups and g…
Figure 12
Figure 12. Figure 12: RMSE of different QoE models (LSTM [60], DEMI [106], DeSVQ [51], DeepQoE [108], MO-QoE [14]) when tested on (a) LFOVIA (b) LIVE Netflix dataset Initially, the neuro-fuzzy framework extracts frame-level features from artifacts and video content, then pools these featur…
Figure 13
Figure 13. Figure 13: (a) Continuous video QoE prediction result on test video sample from LIVE Netflix [49] shown in (b). CI represents the confidence interval. 6. Intelligent and Adaptive Video Streaming Techniques This section provides a review of the broad research efforts directed tow…

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Reference graph

Works this paper leans on

258 extracted references · 78 canonical work pages

  1. [214]

    S. Peng, J. Hu, Z. Li, H. Xiao, S. Yang, C. Xu, Spherical convolution-based saliency detection for FoV prediction in 360-degree video streaming, in: Proc. IEEE International Wireless Communications and Mobile Computing (IWCMC), 2023, pp. 162–167

    work page 2023

  2. [1]

    Ericsson mobility report, [Online] https://www.ericsson.com/49ed78/assets/local/reports-papers/mobility- report/documents/2024/ericsson-mobility-report-june-2024.pdf (July, 2024)

    2024

  3. [2]

    F. E. Subhan, A. Yaqoob, C. H. Muntean, G.-M. Muntean, EDQD: An edge-driven multi-agent DRL solution for improving joint QoE in DASH-based rich media content delivery, in: International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), IEEE, 2024, pp. 1–7

    2024

  4. [3]

    A. A. Simiscuka, M. A. Togou, M. Zorrilla, G.-M. Muntean, 360-ADAPT: An open-RAN-based adaptive scheme for quality enhancement of opera 360 ° content distribution, IEEE Transactions on Green Communications and Networking 8 (3) (2024) 924–938

    2024

  5. [4]

    Brunnstr¨ om, S

    K. Brunnstr¨ om, S. A. Beker, K. De Moor, A. Dooms, S. Egger, M.-N. Garcia, T. Hossfeld, S. Jumisko-Pyykk¨ o, C. Keimel, M.-C. Larabi, et al., Qualinet white paper on definitions of Quality of Experience (2013) 1–20

    2013

  6. [5]

    Document ITU-T G.1080, Quality of Experience requirements for IPTV services, Dec. 2008

    2008

  7. [6]

    M. Xu, C. Li, Z. Chen, Z. Wang, Z. Guan, Assessing visual quality of omnidirectional videos, IEEE Transactions on Circuits and Systems for Video Technology 29 (12) (2019) 3516–3530

    2019

  8. [7]

    Adhuran, C

    J. Adhuran, C. Galkandage, G. Kulupana, A. Fernando, A novel spherical video quality metric for 360° video coding, in: Proc. IEEE International Conference on Quality of Multimedia Experience (QoMEX), 2022, pp. 1–4

    2022

Show all 258 references
  1. [8]

    A. H. Baker, A. Pinard, D. M. Hammerling, On a structural similarity index approach for floating-point data, IEEE Transactions on Visualization and Computer Graphics 30 (9) (2024) 6261–6274

    2024

  2. [9]

    Y. Zhou, M. Yu, H. Ma, H. Shao, G. Jiang, Weighted-to-spherically-uniform SSIM objective quality evaluation for panoramic video, in: Proc. IEEE International Conference on Signal Processing (ICSP), 2018, pp. 54–57

    2018

  3. [10]

    Kalantzis, P

    V. Kalantzis, P. A. Traganitis, Rayleigh-Ritz based updates of the multilinear singular value decomposition, in: Proc. IEEE Asilomar Conference on Signals, Systems, and Computers, 2023, pp. 1059–1063

    2023

  4. [11]

    P. C. Madhusudana, N. Birkbeck, Y. Wang, B. Adsumilli, A. C. Bovik, ST-GREED: Space-time generalized entropic differences for frame rate dependent video quality prediction, IEEE Transactions on Image Processing 30 (2021) 7446–7457

    2021

  5. [12]

    C. G. Bampis, P. Gupta, R. Soundararajan, A. C. Bovik, SpEED-QA: Spatial efficient entropic differencing for image and video quality, IEEE signal processing letters 24 (9) (2017) 1333–1337

    2017

  6. [13]

    L. Wu, X. Zhang, H. Chen, D. Wang, J. Deng, VP-NIQE: An opinion-unaware visual perception natural image quality evaluator, Neurocomputing 463 (2021) 17–28

    2021

  7. [14]

    Ghosh, C

    M. Ghosh, C. Singhal, MO-QoE: Video QoE using multi-feature fusion based optimized learning models, Signal Processing: Image Communication 107 (2022) 116766

    2022

  8. [15]

    S. Chen, Y. Zhang, Y. Li, Z. Chen, Z. Wang, Spherical structural similarity index for objective omnidirectional video quality assessment, in: Proc. IEEE International Conference on Multimedia and Expo (ICME), 2018, pp. 1–6

    2018

  9. [16]

    A. K. Venkataramanan, C. Stejerean, I. Katsavounidis, A. C. Bovik, One transform to compute them all: Efficient fusion-based full-reference video quality assessment, IEEE Transactions on Image Processing 33 (2024) 509–524

    2024

  10. [17]

    Y. Jin, A. Patney, R. Webb, A. C. Bovik, FOVQA: Blind foveated video quality assessment, IEEE Transactions on Image Processing 31 (2022) 4571–4584. 49

    2022

  11. [18]

    Zheng, Z

    Q. Zheng, Z. Tu, X. Zeng, A. C. Bovik, Y. Fan, A completely blind video quality evaluator, IEEE Signal Processing Letters 29 (2022) 2228–2232

    2022

  12. [19]

    J. P. Ebenezer, Z. Shang, Y. Wu, H. Wei, S. Sethuraman, A. C. Bovik, ChipQA: No-reference video quality prediction via space-time chips, IEEE Transactions on Image Processing 30 (2021) 8059–8074

    2021

  13. [20]

    Jiang, W

    N. Jiang, W. Chen, J. Lin, T. Zhao, C.-W. Lin, Video compression artifacts removal with spatial-temporal attention- guided enhancement, IEEE Transactions on Multimedia 26 (2024) 5657–5669

    2024

  14. [21]

    L. Lin, S. Yu, L. Zhou, W. Chen, T. Zhao, Z. Wang, PEA265: Perceptual assessment of video compression artifacts, IEEE Transactions on Circuits and Systems for Video Technology 30 (11) (2020) 3898–3910

    2020

  15. [22]

    Singhal, J

    C. Singhal, J. Chakareski, EVB360: efficient 360-degree video broadcast in next-generation cellular networks, in: Proc. IEEE Intern. Symp. on Broadband Multimedia Systems and Broadcasting, 2022, pp. 1–6

    2022

  16. [23]

    Singhal, I2MB: Intelligent immersive multimedia broadcast in next-generation cellular networks, IEEE Access 10 (2022) 98882–98895

    C. Singhal, I2MB: Intelligent immersive multimedia broadcast in next-generation cellular networks, IEEE Access 10 (2022) 98882–98895

    2022

  17. [24]

    Ascencio, Estimation of the homography matrix to image stitching, Applications of hybrid metaheuristic algo- rithms for image processing (2020) 205–230

    C. Ascencio, Estimation of the homography matrix to image stitching, Applications of hybrid metaheuristic algo- rithms for image processing (2020) 205–230

    2020

  18. [25]

    Bosch, K

    J. Bosch, K. Isteniˇ c, N. Gracias, R. Garcia, P. Ridao, Omnidirectional multicamera video stitching using depth maps, IEEE Journal of Oceanic Engineering 45 (4) (2020) 1337–1352

    2020

  19. [26]

    Tsang, Y.-L

    S.-H. Tsang, Y.-L. Chan, 360-degree intra coding mode for equirectangular projection format videos, in: Proc. IEEE International Symposium on Circuits and Systems (ISCAS), 2020, pp. 1–5

    2020

  20. [27]

    Storch, B

    I. Storch, B. Zatt, L. Agostini, G. Correa, L. A. da Silva Cruz, D. Palomino, Spatially adaptive intra mode pre- selection for ERP 360 video coding, in: Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020, pp. 2178–2182

    2020

  21. [28]

    Y. Wang, Z. Chen, S. Liu, Equirectangular projection oriented intra prediction for 360-degree video coding, in: Proc. IEEE International Conference on Visual Communications and Image Processing (VCIP), 2020, pp. 483–486

    2020

  22. [29]

    Y. Fan, Y. Jin, Z. Meng, X. Zeng, Pixels and panoramas: An enhanced cubic mapping scheme for video \/image- based virtual-reality scenes, IEEE Consumer Electronics Magazine 8 (2) (2019) 44–49

    2019

  23. [30]

    Lin, Y.-H

    J.-L. Lin, Y.-H. Lee, C.-H. Shih, S.-Y. Lin, H.-C. Lin, S.-K. Chang, P. Wang, L. Liu, C.-C. Ju, Efficient projection and coding tools for 360 video, IEEE Journal on Emerging and Selected Topics in Circuits and Systems 9 (1) (Mar.,

  24. [31]

    LIVE-labaratory for Image and Video quality Engineering, an image quality assessment database, in: [Online] Available: http://live.ece.utexas.edu/research/quality/subjective.htm

  25. [32]

    Y. Wang, R. Wang, Z. Wang, K. Fan, Y. Deng, S. Syu, Polar square projection for panoramic video, in: Proc. IEEE Visual Communications and Image Processing (VCIP), 2017, pp. 1–4

    2017

  26. [33]

    Chengjia, Z

    W. Chengjia, Z. Haiwu, S. Xiwu, Octagonal mapping scheme for panoramic video encoding, IEEE Transactions on Circuits and Systems for Video Technology 28 (9) (2018) 2402–2406

    2018

  27. [34]

    Abbas, D

    A. Abbas, D. Newman, Ahg8: rotated sphere projection for 360 video, JVET-F0036, Hobart, AU 31 (2017)

    2017

  28. [35]

    C. Zhou, Z. Li, J. Osgood, Y. Liu, On the effectiveness of offset projections for 360-degree video streaming, ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 14 (3s) (2018) 1–24. 50

    2018

  29. [36]

    H. Hu, Z. Xu, X. Zhang, Z. Guo, Optimal viewport-adaptive 360-degree video streaming against random head movement, in: Proc. IEEE International Conference on Communications (ICC), 2019, pp. 1–6

    2019

  30. [37]

    Singhal, M

    C. Singhal, M. Ghosh, H. Mittal, P. Dewangan, MAIVS: Machine learning based adaptive UHD 360-degree immer- sive video streaming, in: Proc. IEEE International Conference on Communications Workshops (ICC Workshops), 2023, pp. 1082–1087

    2023

  31. [38]

    Zhang, Y

    Y. Zhang, Y. Guan, K. Bian, Y. Liu, H. Tuo, L. Song, X. Li, EPASS360: QoE-aware 360-degree video streaming over mobile devices, IEEE Transactions on Mobile Computing 20 (7) (2020) 2338–2353

    2020

  32. [39]

    X. Chen, T. Tan, G. Cao, Macrotile: Toward QoE-aware and energy-efficient 360-degree video streaming, IEEE Transactions on Mobile Computing 23 (2) (2024) 1112–1126

    2024

  33. [40]

    T. Zhao, Q. Liu, C. W. Chen, QoE in video transmission: A user experience-driven strategy, IEEE Communications Surveys & Tutorials 19 (1) (2016) 285–302

    2016

  34. [41]

    Akhtar, T

    Z. Akhtar, T. H. Falk, Audio-visual multimedia quality assessment: A comprehensive survey, IEEE Access 5 (2017) 21090–21117

    2017

  35. [42]

    M. F. M. Hossain, M. Sarkar, S. H. Ahmed, Quality of Experience for video streaming: A contemporary survey, in: 13th International Wireless Communications and Mobile Computing Conference (IWCMC), 2017, pp. 80–84

    2017

  36. [43]

    Bentaleb, B

    A. Bentaleb, B. Taani, A. C. Begen, C. Timmerer, R. Zimmermann, A survey on bitrate adaptation schemes for streaming media over HTTP, IEEE Communications Surveys and Tutorials 21 (1) (2018) 562–585

    2018

  37. [44]

    Barman, M

    N. Barman, M. G. Martini, QoE modeling for HTTP adaptive video streaming–A survey and open challenges, IEEE Access 7 (2019) 30831–30859

    2019

  38. [45]

    Kougioumtzidis, V

    G. Kougioumtzidis, V. Poulkov, Z. D. Zaharis, P. I. Lazaridis, A survey on multimedia services QoE assessment and machine learning-based prediction, IEEE Access 10 (2022) 19507–19538

    2022

  39. [46]

    C. T. Hewage, A. Ahmad, T. Mallikarachchi, N. Barman, M. G. Martini, Measuring, modeling and integrating time-varying video quality in end-to-end multimedia service delivery: A review and open challenges, IEEE Access 10 (2022) 60267–60293

    2022

  40. [47]

    W. Zhou, X. Min, H. Li, Q. Jiang, A brief survey on adaptive video streaming quality assessment, Journal of Visual Communication and Image Representation 86 (2022) 103526

    2022

  41. [48]

    LIVE-Labaratory for Image and Video quality Engineering, an image quality assessment database, in: Available: http://live.ece.utexas.edu/research/LIVE NFLX II/live nflx plus.html

  42. [49]

    C. G. Bampis, Z. Li, A. K. Moorthy, I. Katsavounidis, A. Aaron, A. C. Bovik, Study of temporal effects on subjective video Quality of Experience, IEEE Transactions on Image Processing 26 (11) (2017) 5217–5231. doi: 10.1109/TIP.2017.2729891

    2017

  43. [50]

    Ghadiyaram, J

    D. Ghadiyaram, J. Pan, A. C. Bovik, A subjective and objective study of stalling events in mobile streaming videos, IEEE Transactions on Circuits and Systems for Video Technology 29 (1) (2017) 183–197

    2017

  44. [51]

    Ghosh, D

    M. Ghosh, D. C. Singhal, R. Wayal, DeSVQ: Deep learning based streaming video QoE estimation, in: Proc. ACM International Conference on Distributed Computing and Networking, 2022, pp. 19–25

    2022

  45. [52]

    Karpov, D

    K. Karpov, D. Kachan, E. Siemens, M. Popova, A comparative study of reliable multidestination data transport protocol with TCP and UDP in a point-to-multipoint streaming environment, in: 2023 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom...

    2023

  46. [53]

    Silhavy, S

    D. Silhavy, S. Pham, S. Arbanowski, S. Steglich, B. Harrer, Latest advances in the development of the open-source player dash.js, in: Proceedings of the 1st Mile-High Video Conference, 2022, pp. 32–38

    2022

  47. [54]

    Spiteri, R

    K. Spiteri, R. Urgaonkar, R. K. Sitaraman, BOLA: Near-optimal bitrate adaptation for online videos, IEEE/ACM Transactions on Networking 28 (4) (2020) 1698–1711

    2020

  48. [55]

    Huang, R

    T.-Y. Huang, R. Johari, N. McKeown, M. Trunnell, M. Watson, A buffer-based approach to rate adaptation: Evidence from a large video streaming service, in: Proc. ACM conference on SIGCOMM, 2014, pp. 187–198

    2014

  49. [56]

    Bross, Y.-K

    B. Bross, Y.-K. Wang, Y. Ye, S. Liu, J. Chen, G. J. Sullivan, J.-R. Ohm, Overview of the versatile video coding (VVC) standard and its applications, IEEE Transactions on Circuits and Systems for Video Technology 31 (10) (2021) 3736–3764

    2021

  50. [57]

    Conviva Inc., Viewer experience report, http://www.conviva.com/conviva-customer-survey-reports/ott- beyond-entertainment-csr/ , online

  51. [58]

    J. Chen, Y. Deng, J. Jia, M. Dohler, A. Nallanathan, Cross-layer QoE optimization for D2D communication in CR-enabled heterogeneous cellular networks, IEEE Transactions on Cognitive Communications and Networking 4 (4) (2018) 719–734

    2018

  52. [59]

    Cisco IBSG Youth Focus Group, Cisco IBSG youth sur, http://www.cisco.com/c/dam/en_us/about/ac79/docs/ ppt/Video_Disruption_SP_Strategies_IBSG.pdf, online

  53. [60]

    Eswara, S

    N. Eswara, S. Ashique, A. Panchbhai, S. Chakraborty, H. P. Sethuram, K. Kuchi, A. Kumar, S. S. Channappayya, Streaming video QoE modeling and prediction: A long short-term memory approach, IEEE Transactions on Circuits and Systems for Video Technology 30 (3) (2020) 661–673

    2020

  54. [61]

    N. Tian, P. Lopes, R. Boulic, A review of cybersickness in head-mounted displays: raising attention to individual susceptibility, Virtual Reality 26 (4) (2022) 1409–1441

    2022

  55. [62]

    Kourtesis, R

    P. Kourtesis, R. Amir, J. Linnell, F. Argelaguet, S. E. MacPherson, Cybersickness, cognition, & motor skills: The effects of music, gender, and gaming experience, IEEE Transactions on Visualization and Computer Graphics 29 (5) (2023) 2326–2336

    2023

  56. [63]

    U. A. Chattha, U. I. Janjua, F. Anwar, T. M. Madni, M. F. Cheema, S. I. Janjua, Motion sickness in virtual reality: An empirical evaluation, IEEE Access 8 (2020) 130486–130499

    2020

  57. [64]

    Dharmasiri, C

    A. Dharmasiri, C. Kattadige, V. Zhang, K. Thilakarathna, Viewport-aware dynamic 360 ° video segment categoriza- tion, in: Proc. ACM Wkshp. NOSSDA V, 2021, p. 114–121

    2021

  58. [65]

    Corbillon, A

    X. Corbillon, A. Devlic, G. Simon, J. Chakareski, Viewport-adaptive navigable 360-degree video delivery, IEEE, Paris, France, 2017

    2017

  59. [66]

    M. Hu, L. Wang, B. Tan, S. Jin, Two-tier 360-degree video delivery control in multiuser immersive communications systems, IEEE Transactions on Vehicular Technology 72 (3) (2023) 4119–4123

    2023

  60. [67]

    S. Kim, H. Oh, C. Kim, eff-HAS: Achieve higher efficiency in data and energy usage on dynamic adaptive streaming, Journal of Communications and Networks 20 (3) (2018) 325–342

    2018

  61. [68]

    Naresh, N

    M. Naresh, N. Gireesh, P. Saxena, M. Gupta, SAC-ABR: Soft actor-critic based deep reinforcement learning for adaptive bitrate streaming, in: Proc. IEEE International Conference on COMmunication Systems & NETworkS (COMSNETS), 2022, pp. 353–361

    2022

  62. [69]

    W. Choi, J. Chen, J. Yoon, Abraider: Multiphase reinforcement learning for environment-adaptive video streaming, IEEE Access 10 (2022) 53108–53123. 52

    2022

  63. [70]

    T. T. Nguyen, N. D. Nguyen, S. Nahavandi, Deep reinforcement learning for multiagent systems: A review of challenges, solutions, and applications, IEEE Transactions on Cybernetics 50 (9) (2020) 3826–3839

    2020

  64. [71]

    Z. Yan, Y. Xu, Data-driven load frequency control for stochastic power systems: A deep reinforcement learning method with continuous action search, IEEE Transactions on Power Systems 34 (2) (2018) 1653–1656

    2018

  65. [72]

    S. A. A. Rizvi, Z. Lin, Reinforcement learning-based linear quadratic regulation of continuous-time systems using dynamic output feedback, IEEE Transactions on Cybernetics 50 (11) (2019) 4670–4679

    2019

  66. [73]

    B. Hou, S. Yang, F. Li, L. Zhu, X. Chen, Y. Wang, X. Fu, NOV A: Neural-optimized viewport adaptive 360-degree video streaming at the edge, IEEE Transactions on Services Computing (2024) 1–15

    2024

  67. [74]

    Singhal, C

    C. Singhal, C. F. Chiasserini, C. Casetti, Efficient multimedia broadcast for heterogeneous users in cellular networks, in: Proc. International Wireless Communications and Mobile Computing Conference (IWCMC), 2016, pp. 315–320

    2016

  68. [75]

    Singhal, C

    C. Singhal, C. F. Chiasserini, C. E. Casetti, EMB: Efficient multimedia broadcast in multi-tier mobile networks, IEEE Transactions on Vehicular Technology 68 (11) (2019) 11186–11199

    2019

  69. [76]

    Q. Wu, H. Li, F. Meng, K. N. Ngan, A perceptually weighted rank correlation indicator for objective image quality assessment, IEEE Transactions on Image Processing 27 (5) (2018) 2499–2513

    2018

  70. [77]

    X. Yu, Z. Tu, Z. Ying, A. C. Bovik, N. Birkbeck, Y. Wang, B. Adsumilli, Subjective quality assessment of user- generated content gaming videos, in: Proc. IEEE/CVF Winter Conference on Applications of Computer Vision, 2022, pp. 74–83

    2022

  71. [78]

    Shang, J

    Z. Shang, J. P. Ebenezer, A. K. Venkataramanan, Y. Wu, H. Wei, S. Sethuraman, A. C. Bovik, A study of subjective and objective quality assessment of HDR videos, IEEE Transactions on Image Processing 33 (2023) 42–57

    2023

  72. [79]

    Saha, Y.-C

    A. Saha, Y.-C. Chen, C. Davis, B. Qiu, X. Wang, R. Gowda, I. Katsavounidis, A. C. Bovik, Study of subjective and objective quality assessment of mobile cloud gaming videos, IEEE Transactions on Image Processing 32 (2023) 3295–3310

    2023

  73. [80]

    J. P. Ebenezer, Z. Shang, Y. Chen, Y. Wu, H. Wei, S. Sethuraman, A. C. Bovik, HDR or SDR? A subjective and objective study of scaled and compressed videos, IEEE Transactions on Image Processing 33 (2024) 3606–3619

    2024

  74. [81]

    X. Yu, Z. Ying, N. Birkbeck, Y. Wang, B. Adsumilli, A. C. Bovik, Subjective and objective analysis of streamed gaming videos, IEEE Transactions on Games 16 (2) (2024) 445–458

    2024

  75. [82]

    A. K. Venkataramanan, Z. Shang, J. P. Ebenezer, M. Chen, Z. Tu, A. C. Bovik, Quality assessment in media and entertainment: Challenges and trends, Computer Vision: Challenges, Trends, and Opportunities (2024) 239

    2024

  76. [83]

    Saini, A

    S. Saini, A. Saha, A. C. Bovik, HIDRO-VQA: High dynamic range oracle for video quality assessment, in: Proc. IEEE/CVF Winter Conference on Applications of Computer Vision, 2024, pp. 469–479

    2024

  77. [84]

    A. K. Venkataramanan, C. Stejerean, I. Katsavounidis, A. C. Bovik, A FUNQUE approach to the quality assessment of compressed HDR videos, in: Picture Coding Symposium (PCS), IEEE, 2024, pp. 1–5

    2024

  78. [85]

    Croci, C

    S. Croci, C. Ozcinar, E. Zerman, J. Cabrera, A. Smolic, Voronoi-based objective quality metrics for omnidirectional video, in: Proc. IEEE International Conference on Quality of Multimedia Experience (QoMEX), 2019, pp. 1–6

    2019

  79. [86]

    Lievens, A

    J. Lievens, A. Munteanu, D. De Vleeschauwer, W. Van Leekwijck, Perceptual video quality assessment in HTTP adaptive streaming, in: Proc. IEEE International Conference on Consumer Electronics (ICCE), 2015, pp. 72–73

    2015

  80. [87]

    M. Taha, A. Ali, J. Lloret, P. R. Gondim, A. Canovas, An automated model for the assessment of QoE of adaptive video streaming over wireless networks, Multimedia Tools and Applications 80 (17) (2021) 26833–26854. 53

    2021

  81. [88]

    Yamagishi, T

    K. Yamagishi, T. Hayashi, Parametric quality-estimation model for adaptive-bitrate-streaming services, IEEE Transactions on Multimedia 19 (7) (2017) 1545–1557

    2017

  82. [89]

    H. T. Tran, T. Vu, N. P. Ngoc, T. C. Thang, A novel quality model for HTTP adaptive streaming, in: Proc. IEEE International Conference on Communications and Electronics (ICCE), 2016, pp. 423–428

    2016

  83. [90]

    G¨ oring, R

    S. G¨ oring, R. R. R. Rao, B. Feiten, A. Raake, Modular framework and instances of pixel-based video quality models for UHD-1/4K, IEEE Access 9 (2021) 31842–31864

    2021

  84. [91]

    Robitza, A

    W. Robitza, A. M. Dethof, S. G¨ oring, A. Raake, A. Beyer, T. Polzehl, Are you still watching? Streaming video quality and engagement assessment in the crowd, in: Proc. IEEE International Conference on Quality of Multimedia Experience (QoMEX), 2020, pp. 1–6

    2020

  85. [92]

    Schiffner, S

    F. Schiffner, S. Moller, Direct scaling and quality prediction for perceptual video quality dimensions, in: Proc. IEEE International Conference on Quality of Multimedia Experience (QoMEX), 2018, pp. 1–3

    2018

  86. [93]

    Singhal, S

    C. Singhal, S. De, R. Trestian, G.-M. Muntean, eWU-TV: User-centric energy-efficient digital TV broadcast over Wi-Fi networks, IEEE Transactions on Broadcasting 61 (1) (2015) 39–55

    2015

  87. [94]

    V. P. M. Kumar, S. Mahapatra, Quality of Experience driven rate adaptation for adaptive HTTP streaming, IEEE Transactions on Broadcasting 64 (2) (2018) 602–620

    2018

  88. [95]

    H. T. T. Tran, N. P. Ngoc, A. T. Pham, T. C. Thang, A multi-factor QoE model for adaptive streaming over mobile networks, in: Proc. IEEE Globecom Workshops (GC Wkshps), 2016, pp. 1–6

    2016

  89. [96]

    Robitza, M.-N

    W. Robitza, M.-N. Garcia, A. Raake, A modular HTTP adaptive streaming QoE model — candidate for ITU-T P.1203 (“P.NATS”), in: Proc. IEEE International Conference on Quality of Multimedia Experience (QoMEX), 2017, pp. 1–6

    2017

  90. [97]

    Recommendation, 1204: Video quality assessment of streaming services over reliable transport for resolutions up to 4K (2023)

    I. Recommendation, 1204: Video quality assessment of streaming services over reliable transport for resolutions up to 4K (2023)

    2023

  91. [98]

    R. R. R. Rao, S. G¨ oring, P. List, W. Robitza, B. Feiten, U. W¨ ustenhagen, A. Raake, Bitstream-based model standard for 4K/UHD: ITU-T P. 1204.3—model details, evaluation, analysis and open source implementation, in: Proc. IEEE International Conference on Quality of Multimedi...

    2020

  92. [99]

    Z. Li, A. Aaron, A. K. Moorthy, A. Manohara, Toward a practical perceptual video quality metric, in: Available: http://techblog.netflix.com/2016/06/toward-practical-perceptual-video.html

    2016

  93. [100]

    P. C. Madhusudana, N. Birkbeck, Y. Wang, B. Adsumilli, A. C. Bovik, ONVIQT: Contrastive video quality esti- mator, IEEE Transactions on Image Processing 32 (2023) 5138–5152

    2023

  94. [101]

    Zhang, A

    F. Zhang, A. Katsenou, C. Bampis, L. Krasula, Z. Li, D. Bull, Enhancing VMAF through new feature integration and model combination, in: 2021 Picture Coding Symposium (PCS), IEEE, 2021, pp. 1–5

    2021

  95. [103]

    Ghosh, C

    M. Ghosh, C. Singhal, Machine learning-based subjective quality estimation for video streaming over wireless networks, in: Next-Generation Wireless Networks Meet Advanced Machine Learning Applications, IGI Global, 2019, pp. 235–254

    2019

  96. [104]

    Casas, S

    P. Casas, S. Wassermann, Improving QoE prediction in mobile video through machine learning, in: Proc. IEEE International Conference on the Network of the Future (NOF), 2017, pp. 1–7. 54

    2017

  97. [105]

    X. Tao, Y. Duan, M. Xu, Z. Meng, J. Lu, Learning QoE of mobile video transmission with deep neural network: A data-driven approach, IEEE Journal on Selected Areas in Communications 37 (6) (2019) 1337–1348

    2019

  98. [106]

    Zadtootaghaj, N

    S. Zadtootaghaj, N. Barman, R. R. R. Rao, S. G¨ oring, M. G. Martini, A. Raake, S. M¨ oller, DEMI: Deep video quality estimation model using perceptual video quality dimensions, in: Proc. IEEE International Workshop on Multimedia Signal Processing (MMSP), 2020, pp. 1–6

    2020

  99. [107]

    Zhang, H

    L. Zhang, H. Dong, A. El Saddik, Towards a QoE model to evaluate holographic augmented reality devices, IEEE MultiMedia 26 (2) (2018) 21–32

    2018

  100. [108]

    Zhang, L

    H. Zhang, L. Dong, G. Gao, H. Hu, Y. Wen, K. Guan, DeepQoE: A multimodal learning framework for video quality of experience (QoE) prediction, IEEE Transactions on Multimedia 22 (12) (2020) 3210–3223

    2020

  101. [109]

    Z. Ying, M. Mandal, D. Ghadiyaram, A. Bovik, Patch-VQ:’Patching up’ the video quality problem, in: Proc. IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 14019–14029

    2021

  102. [110]

    B. Li, W. Zhang, M. Tian, G. Zhai, X. Wang, Blindly assess quality of in-the-wild videos via quality-aware pre- training and motion perception, IEEE Transactions on Circuits and Systems for Video Technology 32 (9) (2022) 5944–5958

    2022

  103. [111]

    X. Guan, F. Li, Y. Zhang, P. C. Cosman, End-to-end blind video quality assessment based on visual and memory attention modeling, IEEE Transactions on Multimedia 25 (2022) 5206–5221

    2022

  104. [112]

    Z. Tu, X. Yu, Y. Wang, N. Birkbeck, B. Adsumilli, A. C. Bovik, RAPIQUE: Rapid and accurate video quality prediction of user generated content, IEEE Open Journal of Signal Processing 2 (2021) 425–440

    2021

  105. [113]

    R. G. de A. Azevedo, N. Birkbeck, I. Janatra, B. Adsumilli, P. Frossard, A viewport-driven multi-metric fusion approach for 360-degree video quality assessment, in: Proc. IEEE International Conference on Multimedia and Expo (ICME), 2020, pp. 1–6

    2020

  106. [114]

    J. Y. Lin, T.-J. Liu, E. C.-H. Wu, C.-C. J. Kuo, A fusion-based video quality assessment (FVQA) index, in: Proc. IEEE Signal and Information Processing Association Annual Summit and Conference (APSIPA), Asia-Pacific, 2014, pp. 1–5

    2014

  107. [115]

    C. G. Bampis, A. C. Bovik, Feature-based prediction of streaming video QoE: Distortions, stalling and memory, Signal Processing: Image Communication 68 (2018) 218–228

    2018

  108. [116]

    M. A. Papadopoulos, A. V. Katsenou, D. Agrafiotis, D. R. Bull, A multi-metric approach for block-level video quality assessment, Signal Processing: Image Communication 78 (2019) 152–158

    2019

  109. [117]

    VQEG HDTV Phase I, in: [Online]

    Video Quality Experts Group (VQEG). VQEG HDTV Phase I, in: [Online]. Available: https://www.its.bldrdoc.gov/vqeg/projects/hdtv/hdtv.aspx, Aug. 15, 2017

    2017

  110. [118]

    Duanmu, K

    Z. Duanmu, K. Zeng, K. Ma, A. Rehman, Z. Wang, A Quality-of-Experience index for streaming video, IEEE Journal of Selected Topics in Signal Processing 11 (1) (2017) 154–166

    2017

  111. [119]

    Phani, M

    V. Phani, M. Ghosh, S. Mahapatra, No-reference video quality assessment from artifacts and content characteristics: A neuro-fuzzy framework for video quality evaluation, Multimedia Tools and Applications 83 (16) (2024) 48049– 48074

    2024

  112. [120]

    Choudhury, S

    A. Choudhury, S. Daly, Combining quality metrics using machine learning for improved and robust HDR image quality assessment, Electronic Imaging 2019 (10) (2019) 307–1

    2019

  113. [121]

    Le Callet, C

    P. Le Callet, C. Viard-Gaudin, D. Barba, A convolutional neural network approach for objective video quality assessment, IEEE Transactions on Neural Networks 17 (5) (2006) 1316–1327. 55

    2006

  114. [122]

    C. Chen, L. K. Choi, G. De Veciana, C. Caramanis, R. W. Heath, A. C. Bovik, Modeling the time—varying subjective quality of HTTP video streams with rate adaptations, IEEE Transactions on Image Processing 23 (5) (2014) 2206–2221

    2014

  115. [123]

    Eswara, K

    N. Eswara, K. Manasa, A. Kommineni, S. Chakraborty, H. P. Sethuram, K. Kuchi, A. Kumar, S. S. Channappayya, A continuous QoE evaluation framework for video streaming over HTTP, IEEE Transactions on Circuits and Systems for Video Technology 28 (11) (2017) 3236–3250

    2017

  116. [124]

    C. G. Bampis, Z. Li, A. C. Bovik, Continuous prediction of streaming video QoE using dynamic networks, IEEE Signal Processing Letters 24 (7) (2017) 1083–1087

    2017

  117. [125]

    Wu, M.-A

    S. Wu, M.-A. Rizoiu, L. Xie, Beyond views: Measuring and predicting engagement in online videos, in: Proc. International AAAI Conference on Web and Social Media, Vol. 12, 2018

    2018

  118. [126]

    X. Tan, Y. Guo, M. A. Orgun, L. Xue, Y. Chen, An engagement model based on user interest and QoS in video streaming systems, Wireless Communications and Mobile Computing 2018 (1) (2018) 1398958

    2018

  119. [127]

    Lebreton, K

    P. Lebreton, K. Yamagishi, Predicting user quitting ratio in adaptive bitrate video streaming, in: IEEE Transactions on Multimedia, 2020, pp. 4526–4540

    2020

  120. [128]

    C. G. Bampis, Z. Li, I. Katsavounidis, A. C. Bovik, Recurrent and dynamic models for predicting streaming video Quality of Experience, IEEE Transactions on Image Processing 27 (7) (2018) 3316–3331

    2018

  121. [129]

    Ghadiyaram, J

    D. Ghadiyaram, J. Pan, A. C. Bovik, Learning a continuous-time streaming video QoE model, IEEE Transactions on Image Processing 27 (5) (2018) 2257–2271

    2018

  122. [130]

    T. N. Duc, C. M. Tran, P. X. Tan, E. Kamioka, Bidirectional LSTM for continuously predicting QoE in HTTP adaptive streaming, in: Proc. 2nd International Conference on Information Science and Systems, 2019, pp. 156–160

    2019

  123. [131]

    T. N. Duc, C. T. Minh, T. P. Xuan, E. Kamioka, Convolutional neural networks for continuous QoE prediction in video streaming services, IEEE Access 8 (2020) 116268–116278

    2020

  124. [132]

    Ul Mustafa, S

    R. Ul Mustafa, S. Ferlin, C. Esteve Rothenberg, D. Raca, J. J. Quinlan, A supervised machine learning approach for DASH video QoE prediction in 5G networks, in: Proc. ACM symposium on QoS and security for wireless and mobile networks, 2020, pp. 57–64

    2020

  125. [133]

    Ghosh, C

    M. Ghosh, C. Singhal, M-3R: A memory based approach for streaming QoE prediction under 3R settings, in: Proc. IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), 2021, pp. 432–437

    2021

  126. [134]

    H. Yang, T. Lin, Y. Zhang, Y. Xu, Z. Chen, J. Yan, Enhancing QoE for multi-device video delivery: A novel dataset and model perspective, IEEE Transactions on Broadcasting (2024) 1–14

    2024

  127. [135]

    LIVE Avvasi dataset, Online: http://live.ece.utexas.edu/research/LIVEStallStudy/index.html

  128. [136]

    Barman, E

    N. Barman, E. Jammeh, S. A. Ghorashi, M. G. Martini, No-reference video quality estimation based on machine learning for passive gaming video streaming applications, IEEE Access 7 (2019) 74511–74527

    2019

  129. [137]

    Zhang, M

    Y. Zhang, M. Yuan, Z. Chen, WHU-MVQoE2016: A Quality of Experience dataset for mobile video research (2016)

    2016

  130. [138]

    LIVE mobile video quality assessment database, Online: http://live.ece.utexas.edu/research/quality/live_ mobile_video.html

  131. [139]

    CSIQ video database

    O. S. U. Laboratory of Computational Perception & Image Quality, http://vision.okstate .edu/csiq/, “CSIQ video database”. 56

  132. [140]

    Zhang, M

    F. Zhang, M. S. Li, M. Lin, M. Y. C. Wong, K. N. Ngan, IVP subjective quality video database, Qualinet Multimedia Databases v6

  133. [141]

    Eswara, D

    N. Eswara, D. S. V. Reddy, S. Chakraborty, H. P. Sethuram, K. Kuchi, A. Kumar, S. S. Channappayya, A linear regression framework for assessing time-varying subjective quality in HTTP streaming, in: Proc. IEEE Global Conference on Signal and Information Processing (GlobalSIP), ...

    2017

  134. [142]

    Eswara, S

    N. Eswara, S. Channappayya, A. Kumar, K. Kuchi, eTVSQ based video rate adaptation in cellular networks with α-fair resource allocation, in: Proc. IEEE Wireless Communications and Networking Conference, 2016, pp. 1–6

    2016

  135. [143]

    Eswara, H

    N. Eswara, H. P. Sethuram, S. Chakraborty, K. Kuchi, A. Kumar, S. S. Channappayya, Modeling continuous video QoE evolution: A state space approach, in: Proc. IEEE International Conference on Multimedia and Expo (ICME), 2018, pp. 1–6

    2018

  136. [144]

    W. Shi, Y. Sun, J. Pan, Continuous prediction for Quality of Experience in wireless video streaming, IEEE Access 7 (2019) 70343–70354

    2019

  137. [145]

    J. J. Quinlan, A. H. Zahran, C. J. Sreenan, Datasets for A VC (H. 264) and HEVC (H. 265) evaluation of dynamic adaptive streaming over HTTP (DASH), in: Proc. of the ACM International Conference on Multimedia Systems, 2016, pp. 1–6

    2016

  138. [146]

    D. Raca, D. Leahy, C. J. Sreenan, J. J. Quinlan, Beyond throughput, the next generation: A 5G dataset with channel and context metrics, in: Proc. ACM multimedia systems conference, 2020, pp. 303–308

    2020

  139. [147]

    MCQoE database, Online: https://github.com/yanghaocuc/mcqoe

  140. [148]

    Seufert, S

    M. Seufert, S. Egger, M. Slanina, T. Zinner, T. Hoßfeld, P. Tran-Gia, A survey on Quality of Experience of HTTP adaptive streaming, IEEE Communications Surveys & Tutorials 17 (1) (2014) 469–492

    2014

  141. [149]

    Juluri, V

    P. Juluri, V. Tamarapalli, D. Medhi, Measurement of Quality of Experience of video-on-demand services: A survey, IEEE Communications Surveys & Tutorials 18 (1) (2015) 401–418

    2015

  142. [150]

    Kreuzberger, B

    C. Kreuzberger, B. Rainer, H. Hellwagner, L. Toni, P. Frossard, A comparative study of DASH representation sets using real user characteristics, in: Proc. ACM International Workshop on Network and Operating Systems Support for Digital Audio and Video, 2016, pp. 1–6

    2016

  143. [151]

    Gadaleta, F

    M. Gadaleta, F. Chiariotti, M. Rossi, A. Zanella, D-DASH: A deep Q-learning framework for DASH video streaming, IEEE Transactions on Cognitive Communications and Networking 3 (4) (2017) 703–718

    2017

  144. [152]

    C. G. Bampis, Z. Li, I. Katsavounidis, T.-Y. Huang, C. Ekanadham, A. C. Bovik, Towards perceptually optimized adaptive video streaming- A realistic Quality of Experience database, IEEE Transactions on Image Processing 30 (2021) 5182–5197

    2021

  145. [153]

    M. Taha, A. Ali, Smart algorithm in wireless networks for video streaming based on adaptive quantization, Con- currency and Computation: Practice and Experience 35 (9) (2023) e7633

    2023

  146. [154]

    H. Mao, R. Netravali, M. Alizadeh, Neural adaptive video streaming with pensieve, in: Proc. ACM Special Interest Group on Data Communication, 2017, pp. 197–210

    2017

  147. [155]

    X. Yin, A. Jindal, V. Sekar, B. Sinopoli, A control-theoretic approach for dynamic adaptive video streaming over HTTP, in: Proc. ACM Conference on Special Interest Group on Data Communication, 2015, pp. 325–338

    2015

  148. [156]

    Ozfatura, O

    E. Ozfatura, O. Ercetin, H. Inaltekin, Optimal network-assisted multiuser DASH video streaming, IEEE Transac- tions on Broadcasting 64 (2) (2018) 247–265. 57

    2018

  149. [157]

    X. Xie, X. Zhang, S. Kumar, L. E. Li, piStream: Physical layer informed adaptive video streaming over LTE, in: Proc. ACM International Conference on Mobile Computing and Networking, 2015, pp. 413–425

    2015

  150. [158]

    M. Xiao, V. Swaminathan, S. Wei, S. Chen, Dash2m: Exploring HTTP/2 for internet streaming to mobile devices, in: Proc. ACM International Conference on Multimedia, 2016, pp. 22–31

    2016

  151. [159]

    Jiang, V

    J. Jiang, V. Sekar, H. Zhang, Improving fairness, efficiency, and stability in HTTP-based adaptive video streaming with festive, in: Proc. IEEE International Conference on Emerging Networking Experiments and Technologies, 2012, pp. 97–108

    2012

  152. [160]

    P. K. Yadav, A. Shafiei, W. T. Ooi, Quetra: A queuing theory approach to DASH rate adaptation, in: Proc. ACM International Conference on Multimedia, 2017, pp. 1130–1138

    2017

  153. [161]

    DASH Industry Forum., Guidelines for implementation: DASH-A VC/264 test cases and vectors., Online: http: //dashif.org/guidelines/

  154. [162]

    HSDPA dataset, [Online] https://qualinet.github.io/databases/commute_path_bandwidth_traces_from_3g_ networks/

  155. [163]

    Jiang, Y

    X. Jiang, Y. Zhang, Perceptual content-aware bitrate adaptation for HTTP streaming using markov decision process, in: Proc. IEEE/ACIS International Fall Conference on Computer and Information Science (ICIS Fall), 2021, pp. 227–231

    2021

  156. [164]

    Zhou, C.-W

    C. Zhou, C.-W. Lin, Z. Guo, mDASH: A Markov decision-based rate adaptation approach for dynamic HTTP streaming, IEEE Transactions on Multimedia 18 (4) (2016) 738–751

    2016

  157. [165]

    J. Kang, K. Chung, Online reinforcement learning based HTTP adaptive streaming scheme, in: Proc. IEEE Inter- national Conference on Information and Communication Technology Convergence (ICTC), 2022, pp. 498–503

    2022

  158. [166]

    Huang, C

    T. Huang, C. Zhou, R.-X. Zhang, C. Wu, X. Yao, L. Sun, Comyco: Quality-aware adaptive video streaming via imitation learning, in: Proc. ACM international conference on multimedia, 2019, pp. 429–437

    2019

  159. [167]

    W. Wu, Y. Gao, T. Zhou, Y. Jia, H. Zhang, T. Wei, Y. Sun, Deep reinforcement learning-based video quality selection and radio bearer control for mobile edge computing supported short video applications, IEEE Access 7 (2019) 181740–181749

    2019

  160. [168]

    Y. Guo, F. R. Yu, J. An, K. Yang, Y. He, V. C. Leung, Buffer-aware streaming in small-scale wireless networks: A deep reinforcement learning approach, IEEE Transactions on Vehicular Technology 68 (7) (2019) 6891–6902

    2019

  161. [169]

    L. Liu, H. Hu, Y. Luo, Y. Wen, When wireless video streaming meets AI: A deep learning approach, IEEE Wireless Communications 27 (2) (2019) 127–133

    2019

  162. [170]

    Yaqoob, G.-M

    A. Yaqoob, G.-M. Muntean, FReD-ViQ: Fuzzy reinforcement learning driven adaptive streaming solution for im- proved video Quality of Experience, IEEE Transactions on Network and Service Management (2024) 1–16

    2024

  163. [171]

    gov/reports-research/reports/measuring-broadband-america/raw-data-measuring-broadband-america-2016 (2016)

    Federal Communications Commission, Raw Data—Measuring Broadband America[Online] https://www.fcc. gov/reports-research/reports/measuring-broadband-america/raw-data-measuring-broadband-america-2016 (2016)

    2016

  164. [172]

    Akhtar, Y

    Z. Akhtar, Y. S. Nam, R. Govindan, S. Rao, J. Chen, E. Katz-Bassett, B. Ribeiro, J. Zhan, H. Zhang, Oboe: Auto-tuning video ABR algorithms to network conditions, in: Proc. Conference of the ACM Special Interest Group on Data Communication, 2018, pp. 44–58

    2018

  165. [173]

    Van Der Hooft, S

    J. Van Der Hooft, S. Petrangeli, T. Wauters, R. Huysegems, P. R. Alface, T. Bostoen, F. De Turck, HTTP/2- based adaptive streaming of HEVC video over 4G/LTE networks, IEEE Communications Letters 20 (11) (2016) 2177–2180. 58

    2016

  166. [174]

    G. Yi, D. Yang, A. Bentaleb, W. Li, Y. Li, K. Zheng, J. Liu, W. T. Ooi, Y. Cui, The ACM multimedia 2019 live video streaming grand challenge, in: Proc. ACM International Conference on Multimedia, 2019, pp. 2622–2626

    2019

  167. [175]

    D. Raca, J. J. Quinlan, A. H. Zahran, C. J. Sreenan, Beyond throughput: A 4G LTE dataset with channel and context metrics, in: Proc. ACM Multimedia Systems Conference, 2018, pp. 460–465

    2018

  168. [176]

    T. C. Nguyen, J.-H. Yun, Predictive tile selection for 360-degree VR video streaming in bandwidth-limited networks, IEEE Communications Letters 22 (9) (2018) 1858–1861

    2018

  169. [177]

    Rossi, L

    S. Rossi, L. Toni, Navigation-aware adaptive streaming strategies for omnidirectional video, in: Proc. IEEE Inter- national Workshop on Multimedia Signal Processing (MMSP), 2017, pp. 1–6

    2017

  170. [178]

    C. Zhou, M. Xiao, Y. Liu, Clustile: Toward minimizing bandwidth in 360-degree video streaming, in: Proc. IEEE Conference on Computer Communications, 2018, pp. 962–970

    2018

  171. [179]

    J. Zou, C. Li, C. Liu, Q. Yang, H. Xiong, E. Steinbach, Probabilistic tile visibility-based server-side rate adaptation for adaptive 360-degree video streaming, IEEE Journal of Selected Topics in Signal Processing 14 (1) (2019) 161–176

    2019

  172. [180]

    Ghosh, V

    A. Ghosh, V. Aggarwal, F. Qian, A rate adaptation algorithm for tile-based 360-degree video streaming, arXiv preprint arXiv:1704.08215 (2017)

    2017 arXiv

  173. [181]

    L. Xie, Z. Xu, Y. Ban, X. Zhang, Z. Guo, 360ProbDash: Improving QoE of 360 video streaming using tile-based HTTP adaptive streaming, in: Proc. ACM International Conference on Multimedia, 2017, pp. 315–323

    2017

  174. [182]

    Y. Guan, C. Zheng, X. Zhang, Z. Guo, J. Jiang, Pano: Optimizing 360 video streaming with a better understanding of quality perception, in: Proc. ACM Special Interest Group on Data Communication, 2019, pp. 394–407

    2019

  175. [183]

    Shafi, W

    R. Shafi, W. Shuai, M. U. Younus, MTC360: A multi-tiles configuration for viewport-dependent 360-degree video streaming, in: Proc. IEEE International Conference on Computer and Communications (ICCC), 2020, pp. 1868– 1873

    2020

  176. [184]

    M. Graf, C. Timmerer, C. Mueller, Towards bandwidth efficient adaptive streaming of omnidirectional video over HTTP: Design, implementation, and evaluation, in: Proc. ACM on Multimedia Systems Conference, 2017, pp. 261–271

    2017

  177. [185]

    H. Wu, D. O. Wu, P. Gong, SR-ABR: Super resolution integrated ABR algorithm for cloud-based video streaming, IEEE Transactions on Emerging Topics in Computational Intelligence (2024) 1–12

    2024

  178. [186]

    H. Liu, Z. Ruan, P. Zhao, C. Dong, F. Shang, Y. Liu, L. Yang, R. Timofte, Video super-resolution based on deep learning: A comprehensive survey, Artificial Intelligence Review 55 (8) (2022) 5981–6035

    2022

  179. [187]

    Sivagami, P

    S. Sivagami, P. Chitra, G. S. R. Kailash, S. Muralidharan, UNet architecture based dental panoramic image segmen- tation, in: Proc. IEEE International Conference on Wireless Communications Signal Processing and Networking (WiSPNET), 2020, pp. 187–191

    2020

  180. [188]

    K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778

    2016

  181. [189]

    Concolato, J

    C. Concolato, J. Le Feuvre, F. Denoual, F. Maz´ e, E. Nassor, N. Ouedraogo, J. Taquet, Adaptive streaming of HEVC tiled videos using MPEG-DASH, IEEE Transactions on Circuits and Systems for Video Technology 28 (8) (2017) 1981–1992

    2017

  182. [190]

    Chiariotti, S

    F. Chiariotti, S. D’Aronco, L. Toni, P. Frossard, Online learning adaptation strategy for DASH clients, in: Proc. ACM International Conference on Multimedia Systems, 2016, pp. 1–12. 59

    2016

  183. [191]

    Sengupta, N

    S. Sengupta, N. Ganguly, S. Chakraborty, P. De, HotDASH: Hotspot aware adaptive video streaming using deep reinforcement learning, in: Proc. IEEE International Conference on Network Protocols (ICNP), 2018, pp. 165–175

    2018

  184. [192]

    Dasari, A

    M. Dasari, A. Bhattacharya, S. Vargas, P. Sahu, A. Balasubramanian, S. R. Das, Streaming 360-degree videos using super-resolution, in: Proc. IEEE Conference on Computer Communications, 2020, pp. 1977–1986

    2020

  185. [193]

    L. Xie, X. Zhang, Z. Guo, Cls: A cross-user learning based system for improving QoE in 360-degree video adaptive streaming, in: Proc. ACM International Conference on Multimedia, 2018, pp. 564–572

    2018

  186. [194]

    Sassatelli, M

    L. Sassatelli, M. Winckler, T. Fisichella, R. Aparicio, User-adaptive editing for 360 degree video streaming with deep reinforcement learning, in: Proc. ACM International Conference on Multimedia, 2019, pp. 2208–2210

    2019

  187. [195]

    J. Fu, X. Chen, Z. Zhang, S. Wu, Z. Chen, 360SRL: A sequential reinforcement learning approach for ABR tile- based 360 video streaming, in: Proc. IEEE International Conference on Multimedia and Expo (ICME), 2019, pp. 290–295

    2019

  188. [196]

    Zhang, P

    Y. Zhang, P. Zhao, K. Bian, Y. Liu, L. Song, X. Li, DRL360: 360-degree video streaming with deep reinforcement learning, in: Proc. IEEE Conference on Computer Communications, 2019, pp. 1252–1260

    2019

  189. [197]

    N. Kan, J. Zou, K. Tang, C. Li, N. Liu, H. Xiong, Deep reinforcement learning-based rate adaptation for adaptive 360-degree video streaming, in: Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019, pp. 4030–4034

    2019

  190. [198]

    N. Kan, J. Zou, C. Li, W. Dai, H. Xiong, RAPT360: Reinforcement learning-based rate adaptation for 360- degree video streaming with adaptive prediction and tiling, IEEE Transactions on Circuits and Systems for Video Technology 32 (3) (2021) 1607–1623

    2021

  191. [199]

    Jiang, X

    Z. Jiang, X. Zhang, Y. Xu, Z. Ma, J. Sun, Y. Zhang, Reinforcement learning based rate adaptation for 360-degree video streaming, IEEE Transactions on Broadcasting 67 (2) (2020) 409–423

    2020

  192. [200]

    Z. Li, P. Zhong, J. Huang, F. Gao, J. Wang, Achieving QoE fairness in bitrate allocation of 360 ° video streaming, IEEE Transactions on Multimedia 26 (2024) 1169–1178

    2024

  193. [201]

    Manfredi, V

    G. Manfredi, V. A. Racanelli, L. De Cicco, S. Mascolo, LSTM-based viewport prediction for immersive video systems, in: Proc. IEEE Mediterranean Communication and Computer Networking Conference (MedComNet), 2023, pp. 49–52

    2023

  194. [202]

    M. T. Islam, C. E. Rothenberg, P. H. Gomes, Predicting XR services QoE with ML: Insights from in-band encrypted QoS features in 360-VR, in: Proc. IEEE International Conference on Network Softwarization (NetSoft), 2023, pp. 80–88

    2023

  195. [203]

    Zhang, Y

    Z. Zhang, Y. Xu, J. Yu, S. Gao, Saliency detection in 360 videos, in: Proc of the ACM European conference on computer vision (ECCV), 2018, pp. 488–503

    2018

  196. [204]

    S. Wang, S. Yang, H. Su, C. Zhao, C. Xu, F. Qian, N. Wang, Z. Xu, Robust saliency-driven quality adaptation for mobile 360-degree video streaming, IEEE Transactions on Mobile Computing 23 (2) (2024) 1312–1329

    2024

  197. [205]

    S.-Z. Qian, Y. Zhang, T. Lin, FBRA360: A fuzzy-based bitrate adaptation scheme for 360 ° video streaming, in: Proc. IEEE International Conference on Multimedia and Expo Workshops (ICMEW), 2023, pp. 122–127

    2023

  198. [206]

    S. M. H. U. Hassan, A. Brennan, G.-M. Muntean, J. McManis, User profile-based viewport prediction using fed- erated learning in real-time 360-degree video streaming, in: Proc. IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), 2023, pp. 1–7. 60

    2023

  199. [207]

    S. Park, A. Bhattacharya, Z. Yang, S. R. Das, D. Samaras, Mosaic: Advancing user Quality of Experience in 360-degree video streaming with machine learning, IEEE Transactions on Network and Service Management 18 (1) (2021) 1000–1015

    2021

  200. [208]

    G. Xiao, M. Wu, Q. Shi, Z. Zhou, X. Chen, DeepVR: Deep reinforcement learning for predictive panoramic video streaming, IEEE Transactions on Cognitive Communications and Networking 5 (4) (2019) 1167–1177

    2019

  201. [209]

    Z. Wang, Z. Luo, M. Hu, M. Chen, D. Wu, Vaser: Optimizing 360-degree live video ingest via viewport-aware neural enhancement, IEEE Transactions on Broadcasting 69 (4) (2023) 927–940

    2023

  202. [210]

    R. I. T. da Costa Filho, M. C. Luizelli, M. T. Vega, J. van der Hooft, S. Petrangeli, T. Wauters, F. De Turck, L. P. Gaspary, Predicting the performance of virtual reality video streaming in mobile networks, in: Proc. ACM Multimedia Systems Conference, 2018, pp. 270–283

    2018

  203. [211]

    Z. Fei, F. Wang, J. Wang, X. Xie, QoE evaluation methods for 360-degree VR video transmission, IEEE Journal of Selected Topics in Signal Processing 14 (1) (2019) 78–88

    2019

  204. [212]

    C. Wu, R. Zhang, Z. Wang, L. Sun, A spherical convolution approach for learning long term viewport prediction in 360 immersive video, in: Proc. of the AAAI Conference on Artificial Intelligence, Vol. 34, 2020, pp. 14003–14040

    2020

  205. [213]

    X. Feng, Z. Bao, S. Wei, Exploring CNN-based viewport prediction for live virtual reality streaming, in: Proc. IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR), 2019, pp. 183–1833

    2019

  206. [215]

    Mahmoud, S

    M. Mahmoud, S. Rizou, A. S. Panayides, N. V. Kantartzis, G. K. Karagiannidis, P. I. Lazaridis, Z. D. Zaharis, A survey on optimizing mobile delivery of 360 ° videos: Edge caching and multicasting, IEEE Access (2023)

    2023

  207. [216]

    Prabavathy, N

    K. Prabavathy, N. S. S. Reddy, C. H. Kumar, K. Sanjayratnam, M. Bharath, Real time shot boundary detection in live 360 degree VR streaming using deep learning, in: Proc. IEEE International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and ...

    2023

  208. [217]

    A. A. Simiscuka, D. A. Ghadge, G.-M. Muntean, Omniscent: An omnidirectional olfaction-enhanced virtual reality 360◦ video delivery solution for increasing viewer quality of experience, IEEE Transactions on Broadcasting 69 (4) (2023) 941–950

    2023

  209. [218]

    Corbillon, F

    X. Corbillon, F. De Simone, G. Simon, 360-degree video head movement dataset, in: Proc. of the 8th ACM on Multimedia Systems Conference, 2017, pp. 199–204

    2017

  210. [219]

    C. Wu, Z. Tan, Z. Wang, S. Yang, A dataset for exploring user behaviors in VR spherical video streaming, in: Proc. 8th ACM on Multimedia Systems Conference, 2017, pp. 193–198

    2017

  211. [220]

    Fremerey, A

    S. Fremerey, A. Singla, K. Meseberg, A. Raake, Avtrack360: An open dataset and software recording people’s head rotations watching 360 ° videos on an HMD, in: Proc. of the 9th ACM multimedia systems conference, 2018, pp. 403–408

    2018

  212. [221]

    Agtzidis, M

    I. Agtzidis, M. Startsev, M. Dorr, 360-degree video gaze behaviour: A ground-truth data set and a classification algorithm for eye movements, in: Proc. ACM International Conference on Multimedia, 2019, pp. 1007–1015

    2019

  213. [222]

    A. T. Nasrabadi, A. Samiei, A. Mahzari, R. P. McMahan, R. Prakash, M. C. Farias, M. M. Carvalho, A taxonomy and dataset for 360 videos, in: Proc. 10th ACM Multimedia Systems Conference, 2019, pp. 273–278. 61

    2019

  214. [223]

    Rossi, C

    S. Rossi, C. Ozcinar, A. Smolic, L. Toni, Do users behave similarly in VR? investigation of the user influence on the system design, ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 16 (2) (2020) 1–26

    2020

  215. [224]

    C. Wu, Z. Tan, Z. Wang, S. Yang, A dataset for exploring user behaviors in VR spherical video streaming, in: Proc. ACM on Multimedia Systems Conference, 2017, pp. 193–198

    2017

  216. [225]

    Available: https://github.com/sajibtariq/360-VR-QoE-In-band-QoS (2023)

    [Online]. Available: https://github.com/sajibtariq/360-VR-QoE-In-band-QoS (2023)

    2023

  217. [226]

    Setayesh, V

    M. Setayesh, V. W. Wong, A content-based viewport prediction framework for 360 ° video using personalized fed- erated learning and fusion techniques, in: Proc. IEEE International Conference on Multimedia and Expo (ICME), 2023, pp. 654–659

    2023

  218. [227]

    S. Wang, S. Yang, H. Li, X. Zhang, C. Zhou, C. Xu, F. Qian, N. Wang, Z. Xu, Salientvr: Saliency-driven mobile 360-degree video streaming with gaze information, in: Proc. ACM 28th Annual International Conference on Mobile Computing And Networking, 2022, pp. 542–555

    2022

  219. [228]

    Online: https://users.ugent.be/jvdrhoof/dataset-4g/

  220. [229]

    Lo, C.-L

    W.-C. Lo, C.-L. Fan, J. Lee, C.-Y. Huang, K.-T. Chen, C.-H. Hsu, 360 video viewing dataset in head-mounted virtual reality, in: Proc. ACM on Multimedia Systems Conference, 2017, pp. 211–216

    2017

  221. [230]

    Y. Xu, Y. Dong, J. Wu, Z. Sun, Z. Shi, J. Yu, S. Gao, Gaze prediction in dynamic 360 immersive videos, in: Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 5333–5342

    2018

  222. [231]

    Eirikur, T

    A. Eirikur, T. Radu, Ntire 2017 challenge on single image super-resolution: Dataset and study, in: Proc. IEEE conference on computer vision and pattern recognition workshops, 2017, pp. 126–135

    2017

  223. [232]

    Netravali, A

    R. Netravali, A. Sivaraman, S. Das, A. Goyal, K. Winstein, J. Mickens, H. Balakrishnan, Mahimahi: accurate record-and-replay for HTTP, in: USENIX Annual Technical Conference (USENIX ATC 15), 2015, pp. 417–429

    2015

  224. [233]

    E. J. David, J. Guti´ errez, A. Coutrot, M. P. Da Silva, P. L. Callet, A dataset of head and eye movements for 360 videos, in: Proc. 9th ACM multimedia systems conference, 2018, pp. 432–437

    2018

  225. [234]

    Speedtest, Accessed:, https://www.speedtest.net/ (Feb. 2019)

    2019

  226. [235]

    Narayanan, E

    A. Narayanan, E. Ramadan, R. Mehta, X. Hu, Q. Liu, R. A. Fezeu, U. K. Dayalan, S. Verma, P. Ji, T. Li, et al., Lumos5G: Mapping and predicting commercial mmwave 5G throughput, in: Proc. ACM internet measurement conference, 2020, pp. 176–193

    2020

  227. [236]

    [Online] https://github.com/salientVR/gazedata

  228. [237]

    Yaqoob, G.-M

    A. Yaqoob, G.-M. Muntean, A combined field-of-view prediction-assisted viewport adaptive delivery scheme for 360° videos, IEEE Transactions on Broadcasting 67 (3) (2021) 746–760

    2021

  229. [238]

    C. G. Bampis, LIVE-Netflix video QoE database, in: Online: http://live.ece.utexas.edu/research/LIVEStallStudy/index.htm

  230. [239]

    UCC daataset, [Online] http://www.cs.ucc.ie/misl/research/datasets/ivid_4g_lte_dataset/

  231. [240]

    UCC 5G daataset, [Online] https://github.com/uccmisl/5Gdataset.git

  232. [241]

    Van Der Hooft, S

    J. Van Der Hooft, S. Petrangeli, T. Wauters, R. Huysegems, P. R. Alface, T. Bostoen, F. De Turck, 4G/LTE bandwidth logs, [Online] :http://users.ugent.be/~jvdrhoof/dataset/

  233. [242]

    Oboe daataset, [Online] https://github.com/USC-NSL/Oboe. 62

  234. [243]

    Baena, O

    C. Baena, O. S. Pe˜ naherrera-Pulla, L. Camacho, R. Barco, S. Fortes, E2E dataset of video streaming and cloud gaming services over 4G and 5G (2022). doi:10.21227/k0w8-qz67

    2022 doi

  235. [244]

    [Online] https://ieee-dataport.org/documents/e2e-dataset-video-streaming-and-cloud-gaming-services- over-4g-and-5g#files

  236. [245]

    [Online] http://dash.ipv6.enstb.fr/headMovements/

  237. [246]

    [Online] https://wuchlei-thu.github.io/

  238. [247]

    [Online] https://github.com/acmmmsys/2018-AVTrack360

    2018

  239. [248]

    [Online] https://gin.g-node.org/ioannis.agtzidis/360_em_dataset

  240. [249]

    [Online] https://github.com/acmmmsys/2019-360dataset

    2019

  241. [250]

    [Online] https://v-sense.scss.tcd.ie/research/3dof/vr_user_behaviour_system_design/

  242. [251]

    [Online] https://github.com/xuyanyu-shh/Saliency-detection-in-360-video

  243. [252]

    [Online] https://github.com/xuyanyu-shh/VR-EyeTracking

  244. [253]

    Dharmasiri, C

    A. Dharmasiri, C. Kattadige, V. Zhang, K. Thilakarathna, Viewport-aware dynamic 360 ° video segment catego- rization, in: Proc. ACM Workshop on Network and Operating Systems Support for Digital Audio and Video, 2021, pp. 114–121

    2021

  245. [254]

    [Online] https://github.com/theamaya/Viewport-Aware-Dynamic-360-Video-Segment-Categorization

  246. [255]

    Opentrack, https://github.com/opentrack/opentrack, online

  247. [256]

    H. S. Rossi, K. Mitra, C. ˚Ahlund, I. Cotanis, N. ¨Ogren, P. Johansson, ALTRUIST: A multi-platform tool for conducting qoe subjective tests, in: Proc. IEEE International Conference on Quality of Multimedia Experience (QoMEX), 2023, pp. 99–102

    2023

  248. [257]

    Cort´ es, P

    C. Cort´ es, P. P´ erez, N. Garc ´ ıa, Unity3D-based app for 360VR subjective quality assessment with customizable questionnaires, in: Proc. IEEE International Conference on Consumer Electronics (ICCE-Berlin), 2019, pp. 281– 282

    2019

  249. [258]

    Singhal, S

    C. Singhal, S. Rafiei, K. Brunnstr¨ om, Real-time live-video streaming in delay-critical application: Remote-controlled moving platform, in: Proc. IEEE Vehicular Technology Conference (VTC-Fall), 2023, pp. 1–7

    2023

  250. [259]

    H. Wang, H. Ning, Y. Lin, W. Wang, S. Dhelim, F. Farha, J. Ding, M. Daneshmand, A survey on the Metaverse: The state-of-the-art, technologies, applications, and challenges, IEEE Internet of Things Journal 10 (16) (2023) 14671–14688. Glossary 63 ABR Adaptive Bit-Rate. AR Augmen...

    2023

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.