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QoE Performance for DASH Videos in a Smart Cache Environment

机译:智能缓存环境中DASH视频的QoE性能

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During the past decade, Internet has seen dramatic increase in video traffic. Users expect a high quality of experience with online video streaming. For video delivery, the DASH (Dynamic Adaptive Streaming over HTTP) standard is one of the common approaches for streaming used by content providers. In order to give users a higher quality of experience, in-network caching and prefetching are useful to reduce video delivery latency with DASH-generated videos. In this paper, we present a Smart Cache framework that uses a cache prefetching scheme that prefetches segment bitrate based on forecasted throughput at the cache entity by using previous throughput values from clients. For our study, we have implemented our framework on the GENI testbed, and our results for single-client and two-client interactions show that Smart Cache increases the byte-hitrate and reduces the number of unused prefetches for cache. We also consider the impact on Quality of Experience (QoE) for each client during contention.
机译:在过去的十年中,Internet的视频流量急剧增加。用户期望在线视频流具有高品质的体验。对于视频传递,DASH(HTTP上的动态自适应流)标准是内容提供商使用的流的常见方法之一。为了给用户带来更高的体验质量,网络内缓存和预取对于减少DASH生成的视频的视频交付延迟很有用。在本文中,我们提出了一个智能缓存框架,该框架使用了缓存预取方案,该方案通过使用客户端以前的吞吐量值,根据缓存实体处的预测吞吐量来预取分段比特率。对于我们的研究,我们已经在GENI测试平台上实现了我们的框架,单客户端和两客户端交互的结果表明,智能缓存提高了字节命中率,并减少了缓存中未使用的预取次数。我们还考虑了争用期间对每个客户的体验质量(QoE)的影响。

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