基于HTTP的自适应视频流集成预取和缓存:一种在线方法

Ke Liang, Jia Hao, Roger Zimmermann, David K. Y. Yau
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引用次数: 28

摘要

我们提出了一个集成的预取和缓存代理,称为iPac,用于基于http的自适应视频流服务,如Netflix和YouTube。我们要解决的挑战是,在代理和内容服务器之间带宽有限的情况下,通过预取来最大化代理的字节命中率。该问题是np困难的,任何最优离线算法所能达到的最佳近似比为1-e—1≈0.63。考虑到离线算法无法应用于具有严格时间约束的实时应用,我们提出了一种新的0.5竞争在线预取算法,据我们所知,该算法具有迄今为止最好的下界。我们通过在亚马逊EC2云上部署iPac来评估它的性能,该云接受部署在PlanetLab上的视频客户端的用户请求,基于用户对YouTube视频请求的真实跟踪。我们的实验结果表明,与最先进的方法相比,iPac在字节命中率(高达84%)和视频速率(高达34%)方面可以显著提高性能。提议的iPac兼容现有的基于http的自适应流实现,而不需要对现有的内容服务器和视频客户端进行任何修改。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrated prefetching and caching for adaptive video streaming over HTTP: an online approach
We present an integrated prefetching and caching proxy, termed iPac, for HTTP-based adaptive video streaming services like Netflix and YouTube. The challenge we address is maximizing the byte-hit ratio for proxies through prefetching in the context of the limited bandwidth between proxies and content servers. The problem is NP-hard, and the best approximation ratio that any optimal offline algorithm can achieve is 1-e--1 ≈ 0.63. Considering that offline algorithms cannot be applied to real-time applications with stringent time constraints, we propose a novel 0.5-competitive online prefetching algorithm which, to the best of our knowledge, has the best lower bound so far. We evaluate the performance of iPac by deploying it over the Amazon EC2 cloud accepting user requests from the video clients deployed on the PlanetLab based on a real trace of user requests for YouTube videos. Our experimental results demonstrate that iPac can significantly improve the performance in terms of byte-hit ratio (up to 84%) and video rates (up to 34%), compared with the state-of-the-art approaches. The proposed iPac is compatible with existing HTTP-based adaptive streaming implementations without requiring any modification to existing content servers and video clients.
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