Design and Evaluation of a Self-Learning HTTP Adaptive Video Streaming Client
Design and Evaluation of a Self-Learning HTTP Adaptive Video Streaming Client
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DOI：
10.1109/lcomm.2014.020414.132649
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发表时间：
2014-04-01
期刊：
影响因子：
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通讯作者：
De Turck, Filip
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文献类型：
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作者：
Claeys, Maxim;Latre, Steven;De Turck, Filip
HTTP自适应流(HAS)正在成为YouTube和Netflix等基于OTT(Over-the-Top)的视频流服务的事实上的标准。通过将视频分割成几秒的多个片段，并以多个质量级别对每个片段进行编码，HAS允许视频客户端在播放期间动态调整所请求的质量，以对网络变化做出反应。然而，最先进的质量选择启发式方法是确定性的，并针对特定的网络配置量身定做。因此，它们无法应对范围广泛的高度动态的网络设置。本文提出并评价了一种基于强化学习(RL)的HAS客户端。自我学习HAS客户端通过与环境交互来动态调整其行为，以优化体验质量(QOE)，即最终用户感知的质量。建议的客户端已经使用基于网络的模拟器进行了全面的评估，并显示在移动网络环境中的性能比传统的HAS客户端高出13%。
HTTP Adaptive Streaming (HAS) is becoming the de facto standard for Over-The-Top (OTT)-based video streaming services such as YouTube and Netflix. By splitting a video into multiple segments of a couple of seconds and encoding each of these at multiple quality levels, HAS allows a video client to dynamically adapt the requested quality during the playout to react to network changes. However, state-of-the-art quality selection heuristics are deterministic and tailored to specific network configurations. Therefore, they are unable to cope with a vast range of highly dynamic network settings. In this letter, a novel Reinforcement Learning (RL)-based HAS client is presented and evaluated. The self-learning HAS client dynamically adapts its behaviour by interacting with the environment to optimize the Quality of Experience (QoE), the quality as perceived by the end-user. The proposed client has been thoroughly evaluated using a network-based simulator and is shown to outperform traditional HAS clients by up to 13% in a mobile network environment.