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Cooperative Caching for Multiple Bitrate Videos in Small Cell Edges

机译:小型小区边缘中多比特率视频的协作缓存

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Caching popular videos at mobile edge servers (MESs) has been confirmed as a promising method to improve mobile users (MUs) perceived quality of experience (QoE) and to alleviate the server load. However, with the multiple bitrate encoding techniques prevalently employed in modern streaming services, caching deployment is challenging for the following three facts: (1) cooperative caching should be explored for MUs located at overlapped coverage areas of MESs; (2) there exists tradeoff consideration for caching either high bitrate videos or high diversity videos; and (3) the relationship between MU perceived QoE and MU received bitrate, known as QoE function, varies in different services. Aiming to maximize the MU perceived QoE, we formulate the multiple bitrate video caching problem, and prove this problem is NP-hard for any given positive and strictly increasing QoE function. We then propose a polynomial complexity algorithm based on a general QoE function, which can achieve an approximate ratio arbitrarily close to 1/2. Specifically, for a linear QoE function, we explore useful property of optimal solutions, based on which more efficient algorithms are proposed. We demonstrate the effectiveness of our solutions via both theoretical analysis and extensive simulations.
机译:已经确认在移动边缘服务器(MES)上缓存流行的视频是提高移动用户(MU)感知体验质量(QoE)并减轻服务器负载的一种有前途的方法。然而,利用现代流服务中普遍使用的多种比特率编码技术,缓存部署对于以下三个事实具有挑战性:(1)对于位于MES重叠覆盖区域的MU,应该探索协作缓存; (2)在缓存高比特率视频或高分集视频时需要权衡考虑; (3)MU感知到的QoE和MU接收到的比特率之间的关系(称为QoE功能)在不同的服务中会有所不同。为了最大化MU感知到的QoE,我们制定了多比特率视频缓存问题,并证明对于任何给定的正向和严格增加的QoE功能,此问题都是NP-难的。然后,我们提出了一种基于一般QoE函数的多项式复杂度算法,该算法可以实现近似接近1/2的近似比率。具体来说,对于线性QoE函数,我们探索了最优解的有用性质,在此基础上提出了更有效的算法。我们通过理论分析和广泛的仿真来证明我们解决方案的有效性。

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