利用移动性预测来实现移动性和流行度缓存以及DASH适应

V. Siris, Xenofon Vasilakos, D. Dimopoulos
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引用次数: 10

摘要

我们展示了我们最近的工作,研究如何利用移动性预测在两个方向上提高移动用户的性能:主动缓存靠近网络附着点的请求内容,移动设备有很大的可能性连接到这些附着点,以及DASH(动态自适应HTTP流)视频质量适应。对于主动缓存,我们讨论了一种基于移动性预测和内容流行度的主动缓存内容的新模型。该模型的一个重要特性是,它根据这两个因素的相对重要性动态地调整缓存决策。对于DASH适应,我们讨论了一个程序,该程序利用移动性和吞吐量预测来选择DASH播放器所要求的视频片段的质量水平,以便在高视频质量和少视频质量开关方面实现改进的QoE。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploiting mobility prediction for mobility & popularity caching and DASH adaptation
We present our recent work investigating how mobility prediction can be exploited for improving the performance of mobile users in two directions: proactive caching requested content close to the network attachment points where a mobile has a high probability to connect to and DASH (Dynamic Adaptive Streaming over HTTP) video quality adaptation. For proactive caching we discuss a new model to proactively cache content based on both mobility prediction and content popularity. An important feature of the model is that it dynamically adapts caching decisions to the relative importance of the two factors. For DASH adaptation we discuss a procedure that exploits mobility and throughput prediction to select the quality levels of video segments requested by a DASH player in order to achieve improved QoE, in terms of both high video quality and few video quality switches.
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