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Channel-Aware Distributed Transmission Control and Video Streaming in UAV Networks

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arxiv 2408.01885 v2 pith:TKKT2BZU submitted 2024-08-03 cs.IT math.IT

classification cs.ITmath.IT
keywords videotransmissionaverageencodingoptimizationpacketdevelopdistributed
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In this paper, we study the problem of distributed transmission control and video streaming optimization for UAVs operating in unlicensed spectrum bands. We develop a rigorous cross-layer analysis framework that jointly considers three inter-dependent factors: (i) in-band interference introduced by ground-aerial nodes at the physical layer, (ii) limited-size queues with delay-constrained packet arrival at the MAC layer, and (iii) video encoding rate at the application layer. First, we formulate an optimization problem to maximize the average throughput by optimizing the fading threshold. To this end, we jointly analyze the queue-related packet loss probabilities as well as the outage probability due to the low SINR. We introduce the DTC algorithm that maximizes the average throughput by adjusting transmission policies to balance the trade-offs between packet drop from queues vs. transmission errors due to low SINRs. Second, we incorporate the video distortion model to develop distributed PSNR optimization for video streaming. The formulated optimization incorporates two cross-layer parameters, specifically the fading threshold and video encoding rate. To tackle this problem, we develop the JDVT-EC algorithm that enhances the average PSNR for all nodes by fine-tuning transmission policies and video encoding rates to balance the trade-offs between packet loss and lossy video compression distortions. Through extensive numerical analysis, we thoroughly examine the proposed algorithms and demonstrate that they are able to find the optimal transmission policies and video encoding rates under various scenarios. Notably, our approach improves the average throughput by 1.7% to 51.65% compared to various baselines. Additionally, we demonstrate an average PSNR increase of 0.24 dB and 1.7 dB compared to separately optimizing the fading threshold and video encoding rate, respectively.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. pFedWN: A Personalized Federated Learning Framework for D2D Wireless Networks with Heterogeneous Data

    cs.LG 2025-01 reject novelty 5.0 of 10

    pFedWN combines channel-aware neighbor selection with an EM-based model weighting step to personalize federated learning over server-free D2D wireless networks.

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