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首页> 外文期刊>IEEE Transactions on Communications >Random Linear Network Coding for Wireless Layered Video Broadcast: General Design Methods for Adaptive Feedback-Free Transmission
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Random Linear Network Coding for Wireless Layered Video Broadcast: General Design Methods for Adaptive Feedback-Free Transmission

机译：无线分层视频广播的随机线性网络编码：自适应无反馈传输的通用设计方法

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摘要

This paper studies the problem of broadcasting layered video streams over heterogeneous single-hop wireless networks using feedback-free random linear network coding (RLNC). We combine RLNC with unequal error protection (UEP) and our main purpose is twofold: to systematically investigate the benefits of UEP+ RLNC layered approach in servicing users with different reception capabilities and to study the effect of not using feedback, by comparing feedback-free schemes with idealistic full-feedback schemes. To these ends, we study “expected percentage of decoded frames” as a key content-independent performance metric and propose a general framework for calculation of this metric, which can highlight the effect of key system, video, and channel parameters. We study the effect of number of layers and propose a scheme that selects the optimum number of layers adaptively to achieve the highest performance. Assessing the proposed schemes with real H.264 test streams, the trade-offs among the users’ performances are discussed and the gain of adaptive selection of number of layers to improve the trade-offs is shown. Furthermore, it is observed that the performance gap between the proposed feedback-free scheme and the idealistic scheme is very small and the adaptive selection of number of video layers further closes the gap.
机译：本文研究了使用无反馈随机线性网络编码（RLNC）在异构单跳无线网络上广播分层视频流的问题。我们将RLNC与不平等错误保护（UEP）结合起来，我们的主要目的是双重的：通过比较无反馈方案，系统地研究UEP + RLNC分层方法在为具有不同接收能力的用户提供服务方面的好处，并研究不使用反馈的影响具有理想的全反馈方案。为此，我们研究了“解码帧的预期百分比”作为与内容无关的关键性能指标，并提出了计算该指标的通用框架，该框架可以强调关键系统，视频和频道参数的影响。我们研究了层数的影响，并提出了一种方案，该方案自适应地选择最佳层数以实现最高性能。利用真实的H.264测试流评估所提出的方案，讨论了用户性能之间的权衡，并显示了自适应选择层数以提高权衡的收益。此外，可以观察到，所提出的无反馈方案和理想方案之间的性能差距很小，视频层数的自适应选择进一步缩小了差距。

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