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QoE-Based Low-Delay Live Streaming Using Throughput Predictions

机译:使用吞吐量预测的基于QoE的低延迟实时流

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Recently, Hypertext Transfer Protocol (HTTP)-based adaptive streaming has become the de facto standard video streaming over the Internet. It allows clients to dynamically adapt media characteristics to the varying network conditions to ensure a high quality of experience (QoE)-that is, minimize playback interruptions while maximizing video quality at a reasonable level of quality changes. In the case of live streaming, this task becomes particularly challenging due to the latency constraints. The challenge further increases if a client uses a wireless access network, where the throughput is subject to considerable fluctuations. Consequently live streams often exhibit latencies of up to 20 to 30 seconds. In the present work, we introduce an adaptation algorithm for HTTP-based live streaming called LOLYPOP (short for low-latency prediction based adaptation), which is designed to operate with a transport latency of a few seconds. To reach this goal, LOINPOP leverages Transmission Control Protocol throughput predictions on multiple time scales, from 1 to 10 seconds, along with estimations of the relative prediction error distributions. In addition to satisfying the latency constraint, the algorithm heuristically maximizes the QoE by maximizing the average video quality as a function of the number of skipped segments and quality transitions. To select an efficient prediction method, we studied the performance of several time series prediction methods in IEEE 802.11 wireless access networks. We evaluated LOLYPOP under a large set of experimental conditions, limiting the transport latency to 3 seconds, against a state-of-the-art adaptation algorithm called FESTIVE. We observed that the average selected video representation index is by up to a factor of 3 higher than with the baseline approach. We also observed that LOLYPOP is able to reach points from a broader region in the QoE space, and thus it is better adjustable to the user profile or service provider requirements.
机译:最近,基于超文本传输​​协议(HTTP)的自适应流已成为Internet上事实上的标准视频流。它允许客户动态地使媒体特性适应不断变化的网络状况,以确保高质量的体验(QoE),即在合理的质量变化水平下最大程度地减少播放中断,同时最大程度地提高视频质量。在实时流传输的情况下,由于延迟限制,此任务变得特别具有挑战性。如果客户端使用无线接入网络,那么吞吐量将受到很大的波动,挑战将进一步加剧。因此,直播流通常会出现长达20到30秒的延迟。在当前的工作中,我们为基于HTTP的实时流引入一种称为LOLYPOP(基于低延迟预测的自适应的简称)的自适应算法,该算法旨在以几秒钟的传输延迟运行。为了实现此目标,LOINPOP在1到10秒的多个时间范围内利用了传输控制协议吞吐量预测,以及相对预测误差分布的估计。除了满足等待时间约束之外,该算法还通过根据跳过的段数和质量转换使平均视频质量最大化来启发式地最大化QoE。为了选择一种有效的预测方法,我们研究了几种时间序列预测方法在IEEE 802.11无线访问网络中的性能。我们针对称为FESTIVE的最新自适应算法,在大量实验条件下将LOLYPOP评估为3秒,将运输延迟限制为3秒。我们观察到,平均选定视频表示指标比基准方法高出3倍。我们还观察到,LOLYPOP能够到达QoE空间中更广阔区域的点,因此可以更好地调整以适应用户配置文件或服务提供商的要求。

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