Statistical QoS-driven power adaptation for distributed caching based mobile offloading over 5G wireless networks

To support the emerging next era of mobile wireless networks, researchers have made a great deal of efforts in promising techniques in multimedia services — the statistical quality of service (QoS) technique, which has been proved to be effective in statistically guaranteeing delay-bounded video transmissions over the time-varying wireless channels. On the other hand, as the 5G-promising techniques, the device-to-device (D2D) assisted mobile traffic offloading and caching techniques have also been proposed to significantly improve the overall system capacity as well as mitigate the traffic congestion at the base stations for the real-time traffic over 5G wireless networks. However, how to efficiently integrate these advanced techniques in supporting the statistical QoS requirements have imposed many new challenges not met before. To effectively overcome the aforementioned problems, in this paper we propose the statistical QoS-driven power adaptation scheme for the distributed caching assisted mobile offloading scheme over 5G wireless networks. In particular, under the Nakagami-m fading model, we establish the system models for the D2D assisted caching and the partial in-network transcoding. Given the statistical QoS constraints, we derive and analyze the effective capacity under our developed optimal adaptation policies for cache-enabled D2D assisted mobile offloading system under 5G wireless networks. Also conducted is a set of simulations which show that our proposed scheme outperforms the other existing schemes to efficiently implement the statistical QoS requirements over cache-enabled 5G wireless networks.