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.

中文翻译：

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小小区边缘多比特率视频的协同缓存

在移动边缘服务器 (MES) 缓存流行视频已被证实是一种有前途的方法，可以提高移动用户 (MU) 的感知体验质量 (QoE) 并减轻服务器负载。然而，随着现代流媒体服务中普遍采用的多比特率编码技术，缓存部署面临以下三个方面的挑战：(1) 应该为位于 MES 重叠覆盖区域的 MU 探索协作缓存；(2) 存在缓存高码率视频或高分集视频的权衡考虑；(3) MU 感知 QoE 和 MU 接收比特率之间的关系，称为 QoE 函数，在不同的服务中有所不同。为了最大化 MU 感知 QoE，我们制定了多比特率视频缓存问题，并证明这个问题对于任何给定的正且严格增加的 QoE 函数都是 NP-hard 问题。然后我们提出了一种基于通用QoE函数的多项式复杂度算法，可以实现任意接近1/2的近似比率。具体来说，对于线性 QoE 函数，我们探索了最佳解决方案的有用特性，并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模拟证明了我们的解决方案的有效性。