To support multimedia applications, Mobile Edge Computing (MEC) servers offer storage and computing capacities to handle videos close to end-users. However, the high load in peak hours consumes the limited available bandwidth of existing cellular and backhaul links leading to low network performance. Hence, an elastic system model is required to maintain the high Quality of Experience (QoE) as the resource demands increase. Caching popular videos at mobile devices is considered a promising technique for content delivery. Yet, mobile users offer small capacities that are not adequate for large-sized video sharing. In this paper, we extend the collaborative caching and processing framework in edge networks (Collaborative Edge - CE) to include the users' mobile video sharing (Device-to-Device - D2D). We propose a caching strategy to cache only the chunks of videos to be watched and instead of offloading one video content by one edge node, helpers (MEC servers and users) will collaborate to store and share different chunks to optimize the storage/transmission resources usage. To only cache popular contents, we designed a D2D-aware proactive chunks caching on users’ devices based on our chunks popularity model. Next, we formulate this CE-D2D collaborative problem as a linear program. Due to the NP-hardness of the problem, we introduce a sub-optimal relaxation and an online heuristic using the proactive caching and presenting a near optimal data offloading and a profitable payment determination, with polynomial time complexity. The simulation results show that our policies and heuristics outperform other edge caching approaches by more than 10% in terms of hit ratio, average delay, and cost.

为了支持多媒体应用程序，移动边缘计算（MEC）服务器提供存储和计算功能，以处理接近最终用户的视频。但是，高峰时段的高负载消耗了现有蜂窝和回程链路的有限可用带宽，从而导致网络性能低下。因此，随着资源需求的增加，需要一个弹性系统模型来维持高质量的体验（QoE）。在移动设备上缓存流行的视频被认为是一种有前途的内容交付技术。但是，移动用户提供的小容量不足以进行大型视频共享。在本文中，我们扩展了边缘网络中的协作缓存和处理框架（Collaborative Edge-CE），以包括用户的移动视频共享（Device-to-Device-D2D）。我们提出了一种缓存策略，仅缓存要观看的视频块，而不是通过一个边缘节点卸载一个视频内容，助手（MEC服务器和用户）将协作存储和共享不同的块，以优化存储/传输资源的使用。为了仅缓存流行的内容，我们基于块流行度模型设计了一种D2D感知的主动块在用户设备上进行缓存。接下来，我们将此CE-D2D协作问题表述为线性程序。由于问题的NP难点，我们使用主动缓存引入了次优松弛和在线启发式方法，并提供了多项最佳时间复杂度的近乎最佳的数据分载和可盈利的付款确定。