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Distributed Caching Algorithms in the Realm of Layered Video Streaming

机译:分层视频流领域中的分布式缓存算法

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Distributed caching architectures have been proposed for bringing content close to requesters, and the key problem is to design caching algorithms for reducing content delivery delay, which determines to an extent the user Quality of Experience (QoE). This problem obtains an interesting new twist with the advent of advanced layered-video encoding techniques such as Scalable Video Coding. In this paper, we show that the problem of finding the caching configuration of video encoding layers that minimizes delivery delay for a network operator is NP-Hard, and we establish a pseudopolynomial-time optimal solution by using a connection with the multiple-choice knapsack problem. Next, we design caching algorithms for multiple network operators that cooperate by pooling together their co-located caches, in an effort to aid each other, so as to avoid large delays due to fetching content from distant servers. We derive an approximate solution to this cooperative caching problem by using a technique that partitions the cache capacity into amounts dedicated to own and other operators' caching needs. Trace-driven evaluations demonstrate up to 25 percent reduction in delay over existing caching schemes. As a side benefit, our algorithms achieve smoother playback for video streaming applications, with fewer playback stalls and higher decoded quality.
机译:已经提出了用于使内容接近请求者的分布式缓存体系结构,并且关键问题是设计用于减少内容递送延迟的缓存算法,该缓存算法在一定程度上确定了用户的体验质量(QoE)。随着高级分层视频编码技术(例如可伸缩视频编码)的出现,此问题获得了有趣的新变化。在本文中,我们表明发现找到视频编码层的缓存配置以使网络运营商的交付延迟最小的问题是NP-Hard,并且通过使用与多项选择背包的连接来建立伪多项式时间最优解决方案。问题。接下来,我们为多个网络运营商设计缓存算法,这些网络运营商通过将它们位于同一地点的缓存池在一起来进行协作,以互相帮助,从而避免了由于从远程服务器获取内容而造成的大延迟。通过使用一种将缓存容量划分为专用于自己和其他运营商的缓存需求的数量的技术,我们得出了对此协作缓存问题的一种近似解决方案。跟踪驱动的评估表明,与现有缓存方案相比,延迟最多可减少25%。附带的好处是,我们的算法可为视频流应用程序实现更流畅的播放,并减少播放停顿和提高解码质量。

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