细粒度可扩展的视频缓存

Qiushi Gong, J. Woods, K. Kar, Jacob Chakareski
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引用次数: 4

摘要

缓存已被证明可以提高网络性能。本文主要研究细粒度可扩展视频缓存技术。我们从单个缓存场景开始,提供了一个缓存分配问题的解决方案,该解决方案优化了最受欢迎的视频剪辑的平均预期视频质量。实际跟踪数据应用于验证我们的算法的性能,并比较其回程链路带宽消耗相对于不可扩展的视频缓存。此外,我们将我们的分析扩展到协作缓存和集成网络编码,以进一步提高传输效率。我们的实验结果显示了相当大的性能提升。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fine-Grained Scalable Video Caching
Caching has been shown to enhance network performance. In this paper, we study fine-grain scalable video caching. We start from a single cache scenario by providing a solution to the caching allocation problem that optimizes the average expected video quality for the most popular video clips. Actual trace data is applied to verify the performance of our algorithm and compare its backhaul link bandwidth consumption relative to non-scalable video caching. In addition, we extend our analysis to collaborative caching and integrate network coding for further transmission efficiency. Our experimental results demonstrate considerable performance enhancement.
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