Optimized resource allocation for multipath cooperative video transmission over MEC-assisted 5G heterogeneous networks

With the continuously increasing demands for video application, high-quality video transmission will still be one of the major challenges for future Mobile Edge Computing (MEC) assisted 5G heterogeneous networks. It poses the pressing need for solutions that enable to optimize the resource allocation. Therefore, we design a complete and practical multipath cooperative transmission scheme for scalable video, to achieve efficient operation of cache management, resource optimization, signaling interaction and information processing. On this basis, an improved cache optimization algorithm is proposed to determine an appropriate cooperative cache strategy for all MEC severs. By considering the structural features of SVC, content-related rate-quality relationship and the delay characteristics of multipath cooperative transmission, it can effectively improve cache hit ratio while decreasing the average delay. For further improving the overall QoE of all clients, a novel transmission resource optimization problem that aims to maximize the sum of the short-time quality gain and the final quality achievement of all the clients, is formulated. To accurately predict the final quality achievement, we establish a low-complexity model for real-time analysis of the end-to-end delay, and subsequently propose an innovative quality prediction method with considering deadline constraint. Based on the formulation, a two-phase algorithm is developed to address the problem efficiently. The simulation results show that our scheme outperforms the others in the caching optimization stage in terms of both hit rate and delay, and moreover achieves the playback continuity while guaranteeing higher video quality in the transmission optimization stage.