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Real Time Video Quality Representation Classification of Encrypted HTTP Adaptive Video Streaming - the Case of Safari

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Ran Dubin, Amit Dvir, Ofir Pele, Ofer Hadar, Itay Richman, Ofir Trabelsi

Multimedia · Submitted 1 Feb 2016

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We present a new method and show for the first time that video quality representation classification for (YouTube) encrypted HTTP adaptive streaming is possible.

how it is stated · asserted 0.83 read the record →

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arXiv ID1602.00489 (version 2)
Pith IDIFBGHEO4, Pith's permanent record of this paper and its review
CitationAmit Dvir, Itay Richman, Ofer Hadar, Ofir Pele, Ofir Trabelsi, Ran Dubin. "Real Time Video Quality Representation Classification of Encrypted HTTP Adaptive Video Streaming - the Case of Safari." arXiv:1602.00489 (2016). https://pith.science/paper/IFBGHEO4
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 6,098 input tokens.

What the abstract itself states, as probabilities: claims first 0.94 · states limitation 0.24 · human participants 0.05 · controlled comparison 0.06 · randomized assignment 0.02 · states quantitative result 0.99 · compares with prior results 0.09 · mentions code or data availability 0.04.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

The increasing popularity of HTTP adaptive video streaming services has dramatically increased bandwidth requirements on operator networks, which attempt to shape their traffic through Deep Packet Inspection (DPI). However, Google and certain content providers have started to encrypt their video services. As a result, operators often encounter difficulties in shaping their encrypted video traffic via DPI. This highlights the need for new traffic classification methods for encrypted HTTP adaptive video streaming to enable smart traffic shaping. These new methods will have to effectively estimate the quality representation layer and playout buffer. We present a new method and show for the first time that video quality representation classification for (YouTube) encrypted HTTP adaptive streaming is possible. We analyze the performance of this classification method with Safari over HTTPS. Based on a large number of offline and online traffic classification experiments, we demonstrate that it can independently classify, in real time, every video segment into one of the quality representation layers with 97.18% average accuracy.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 The increasing popularity of HTTP adaptive video streaming services has dramatically increased bandwidth requirements on operator networks, which attempt to shape their traffic through Deep Packet Inspection (DPI). background 1.00 0.05 0.02 asserted 1.00
s1 However, Google and certain content providers have started to encrypt their video services. background 0.99 0.05 0.02 asserted 0.99
s2 As a result, operators often encounter difficulties in shaping their encrypted video traffic via DPI. background 0.95 0.07 0.03 asserted 0.84
s3 This highlights the need for new traffic classification methods for encrypted HTTP adaptive video streaming to enable smart traffic shaping. gap 0.73 0.05 0.07 asserted 0.82
s4 These new methods will have to effectively estimate the quality representation layer and playout buffer. background 0.53 0.07 0.05 asserted 0.64
s5 We present a new method and show for the first time that video quality representation classification for (YouTube) encrypted HTTP adaptive streaming is possible. contribution 1.00 0.96 0.79 asserted 0.83
s6 We analyze the performance of this classification method with Safari over HTTPS. contribution 0.67 0.93 0.04 asserted 0.93
s7 Based on a large number of offline and online traffic classification experiments, we demonstrate that it can independently classify, in real time, every video segment into one of the quality representation layers with 97.18% average accuracy. result 1.00 0.97 0.76 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 1.00.

What was examined, and what was not

sources read
  • arXiv listing abstract v2
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

The increasing popularity of HTTP adaptive video streaming services has dramatically increased bandwidth requirements on operator networks, which attempt to shape their traffic through Deep Packet Inspection (DPI). However, Google and certain content providers have started to encrypt their video services. As a result, operators often encounter difficulties in shaping their encrypted video traffic via DPI. This highlights the need for new traffic classification methods for encrypted HTTP adaptive video streaming to enable smart traffic shaping. These new methods will have to effectively estimate the quality representation layer and playout buffer. We present a new method and show for the first time that video quality representation classification for (YouTube) encrypted HTTP adaptive streaming is possible. We analyze the performance of this classification method with Safari over HTTPS. Based on a large number of offline and online traffic classification experiments, we demonstrate that it can independently classify, in real time, every video segment into one of the quality representation layers with 97.18% average accuracy.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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