While mobile networks are evolving rapidly, the battle between ever-growing traffic demands and out-paced network capacities will continue and require more efficient solutions. Emerging techniques such as mobile edge computing and device-to-device (D2D) communications can help relieve traffic at the mobile edge and accommodate surging traffic demands from various content-centric services. In this work, we focus on exploiting device caching and user collaboration to offload content distribution traffic. Specifically, we investigate the request offloading problem, which aims to appropriately select caching devices and maximize the content requests that can be fulfilled through D2D communications. Given the constraints of individual transmission and caching capacities, the number of available D2D channels, and information privacy with social-awareness, we can decouple the request offloading problem into two subproblems, i.e., the device caching and matching problem, and the D2D channel allocation problem. As we prove that both problems are NP-hard, we propose efficient algorithms that iteratively make a best local decision in each step. Simulation results show that the proposed algorithms perform fairly closely to optimal solutions in small-scale instances and outperform the reference schemes under various situations.

中文翻译：

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通过设备到设备通信协助进行内容分发的流量分流

在移动网络快速发展的同时，不断增长的流量需求与超速网络容量之间的斗争将继续，并需要更有效的解决方案。诸如移动边缘计算和设备到设备（D2D）通信之类的新兴技术可以帮助缓解移动边缘的流量，并适应各种以内容为中心的服务对流量的激增需求。在这项工作中，我们专注于利用设备缓存和用户协作来减轻内容分发流量的负担。具体来说，我们研究了请求卸载问题，该问题旨在适当地选择缓存设备并最大化可通过D2D通信实现的内容请求。考虑到各个传输和缓存能力的限制，可用D2D通道的数量，以及具有社会意识的信息隐私，我们可以将请求卸载问题分解为两个子问题，即设备缓存和匹配问题以及D2D通道分配问题。当我们证明这两个问题都是NP问题时，我们提出了高效的算法，可在每个步骤中反复做出最佳的局部决策。仿真结果表明，所提出的算法在小规模情况下与最优解的性能相当接近，并且在各种情况下均优于参考方案。