Using Viewing Statistics to Control Energy and Traffic Overhead in Mobile Video Streaming
Using Viewing Statistics to Control Energy and Traffic Overhead in Mobile Video Streaming
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DOI：
10.1109/tnet.2015.2415873
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发表时间：
2016-06
期刊：
影响因子：
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通讯作者：
M. Siekkinen;M. A. Hoque;J. Nurminen
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文献类型：
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作者：
M. Siekkinen;M. A. Hoque;J. Nurminen
视频流会很快耗尽智能手机电池的电量。无线通信消耗了很大一部分能量。在本文中，我们首先研究了服务提供商使用的不同视频内容交付策略的能源效率，并确定了一些能源效率低下的来源。具体来说，我们发现在预取小块和大块视频内容之间存在能源浪费的基本权衡：小块不好，因为每次下载都会导致固定的尾部能量消耗，而不管下载的内容有多少，而大块会增加下载数据的风险，因为用户会放弃视频而永远不会查看这些数据。因此，最佳策略的关键在于预测用户何时可能过早放弃观看的能力。然后，我们提出了一种名为esschedule的算法，该算法使用观看统计数据来预测观看者的行为，并为给定的移动客户端计算能量最佳下载策略。该算法还包括一种明确控制流量开销的机制，即用户永远不会观看的不必要的内容下载。我们的评估结果表明，与其他策略相比，该算法可以将能源浪费减少到一半以下。我们还展示并试验了一个Android原型，它将esschedule集成到YouTube下载器中。
Video streaming can drain a smartphone battery quickly. A large part of the energy consumed goes to wireless communication. In this article, we first study the energy efficiency of different video content delivery strategies used by service providers and identify a number of sources of energy inefficiency. Specifically, we find a fundamental tradeoff in energy waste between prefetching small and large chunks of video content: small chunks are bad because each download causes a fixed tail energy to be spent regardless of the amount of content downloaded, whereas large chunks increase the risk of downloading data that user will never view because of abandoning the video. Hence, the key to optimal strategy lies in the ability to predict when the user might abandon viewing prematurely. We then propose an algorithm called eSchedule that uses viewing statistics to predict viewer behavior and computes an energy optimal download strategy for a given mobile client. The algorithm also includes a mechanism for explicit control of traffic overhead, i.e., unnecessary download of content that the user will never watch. Our evaluation results suggest that the algorithm can cut the energy waste down to less than half compared to other strategies. We also present and experiment with an Android prototype that integrates eSchedule into a YouTube downloader.