Heterogeneous Statistical QoS-Driven Resource Allocation for D2D Cluster-Caching Based 5G Multimedia Mobile Wireless Networks Conference Paper uri icon

abstract

  • © 2018 IEEE. To support the multimedia services over 5G mobile wireless networks, the heterogeneous statistical quality- of-service (QoS) technique has been designed to jointly guarantee the statistically delay-bounded video transmissions over different time-varying wireless channels, simultaneously. On the other hand, as one of the 5G-promising candidate techniques, device-to-device (D2D) technique has been shown to improve both energy efficiency and spectrum efficiency for multimedia communications. However, overuse of the D2D transmissions may cause the unnecessary interferences to the original base-station oriented cellular networks. Consequently, under heterogeneous statistical delay- bounded QoS constraints, in-network caching techniques, clustering algorithms, and resource allocation policies have been proposed for D2D cluster-caching based 5G multimedia wireless networks with new opportunities and challenges. To effectively overcome the above-mentioned challenges, we propose the heterogeneous statistical QoS-driven resource allocation scheme through applying the D2D cluster-caching based system. In particular, under the Nakagami-textit m fading model, we establish the system models for the dynamic D2D clustering based video stream sharing and the wireless transmissions. Given the heterogeneous statistical QoS constraints, we derive and analyze the aggregate effective capacity under our developed optimal resource allocation policies for the heterogeneous QoS-driven D2D cluster-caching based 5G multimedia mobile wireless networks. Also conducted is a set of simulations which validate and evaluate our proposed D2D cluster-caching based scheme, compared with the other existing schemes in terms of effective capacity under heterogeneous statistical QoS constraints.

author list (cited authors)

  • Zhang, X. i., & Wang, J.

citation count

  • 3

publication date

  • May 2018

publisher