Heterogeneous Statistical QoS-Driven Power Allocation for Collaborative D2D Caching Over Edge-Computing Networks

Abstract

With the exponentially increasing demand for wireless multimedia services over 5G mobile wireless networks, the statistical quality-of-service (QoS) provisioning has been proven to be able to effectively guarantee the multimedia data transmissions over highly time-varying wireless channels. On the other hand, many research efforts have been focused on various 5G-promising candidate techniques, such as device-to-device (D2D) caching based communications to offload cellar traffics and address the data explosion problem for the next generation wireless networks. Furthermore, in order to enhance the reliability of video delivery without causing too much interference to other mobile users, researchers have applied the coordinated joint transmission techniques for collaborative D2D caching scheme, which enables mobile users to share popular multimedia files within a D2D communication group instead of downloading from remote backhaul networks. Towards this end, one of the key issues lies in the power allocation problems subject to the heterogeneous statistical delay-bounded QoS requirements for the collaborative D2D caching over edge-computing networks. To overcome the aforementioned challenges, in this paper we propose the collaborative D2D caching model and D2D communication schemes over edge-computing networks. Under the heterogeneous statistical delay-bounded QoS requirements, we formulate and solve the effective-capacity optimization problem for our proposed collaborative D2D caching schemes over edge-computing networks. Then, we develop the collaborative D2D-cache matching algorithms by using bipartite graph technique for selecting D2D-caching users to maximize the effective capacity. Also conducted is a set of simulations which evaluate the system performances and show that our proposed collaborative D2D caching schemes outperform the other existing schemes under heterogeneous statistical delay-bounded QoS constraints.