Edge Selective Sharing for Massive Mobile Video Streaming With Cross-Layer Optimization

In this paper, we propose an edge selective sharing architecture (ESSA) for massive mobile video streaming (MMVS) and develop cross-layer optimizations for joint user scheduling and power allocation (JUSPA) in ESSA. This work aims to ensure users' high quality of experience (QoE) by efficiently using network resources over mobile edge computing (MEC)-assisted massive multiple-input and multiple-output (MIMO) networks. Initially, by taking advantage of the analytical and empirical characteristics of video streaming, ESSA is able to maintain MMVS by activating only a portion of facilities in wireless access networks (WANs) without sacrificing video quality. Following this, a cross-layer optimization problem is formulated for JUSPA. We simplify the problem into a generalized assignment (GA) problem by discussing the peculiarities of MMSV, whose approximate solution can be obtained in polynomial time. Moreover, load balancing based on user density is integrated to reformulate a capacitated facility location (CFL) problem solvable with low overall time complexity, effectively mitigating the increased overheads of MEC units and enhancing the overall performance of MMVS. Numerical results indicate that our ESSA with JUSPA schemes for MMVS achieves better streaming fluency and transmission efficiency than alternative methods.