Cross-Layer Joint Optimization Algorithm for Adaptive Video Streaming in MEC- Enabled Wireless Networks

Abstract

HTTP Adaptive Streaming (HAS) is one of the most promising solutions for delivering streaming video services to users. However, due to its client-driven nature, it is challenging for telecom operators and mobile service providers to guarantee a certain level of quality of experience (QoE) for their mobile users when using HAS. Researches have shown that when various HAS clients compete simultaneously to obtain bandwidth resources from a shared network, this leads to unfairness and under-utilization of the system and degrades customers' QoE. In order to address these issues and provide some coordination between HAS clients, this paper introduces an innovative solution which employs a recently introduced MPEG solution, called Server and Network Assisted Dynamic Adaptive Streaming over HTTP (SAND-DASH) and Multi-Access Edge Computing (MEC) support in the existing wireless network environment. The solution is based on an optimisation algorithm which maximizes QoE, fairness, and system utilization while considering the radio resource constraints. The paper formulates a novel cross-layer joint optimization model which considers QoE, fairness, system utilization, and radio resource limitation. Then it describes the MEC-OP-SA, an efficient online greedy-based algorithm for solving the modeled optimization problem. The results indicate that the proposed algorithm, MEC-OP-SA, improves the overall system utilization and fairness while maintaining a high QoE level for HAS clients compared to existing solutions.