Edge Computing Assisted Joint Quality Adaptation for Mobile Video Streaming

Most studies on adaptive streaming over Hypertext Transport Protocol (HTTP) have focused on improving the quality of experience (QoE) of clients by running the rate adaptation algorithm on the client side. In a cellular environment, this leads to inefficient resource utilization because of the lack of coordination between the competing clients. In cellular networks, the key challenge for HTTP adaptive streaming (HAS) is to optimize the conflicting video quality objectives. Edge cloud-assisted adaptive streaming presents an opportunity to optimize the quality of experience in cellular networks by moving the adaptation intelligence from the client to the edge cloud. HAS algorithms select the video quality based on the estimated throughput and playback buffer level. In this paper, we first present a joint throughput estimation method for HAS by taking advantage of mobile edge computing. Next, we present an optimized solution for multi-access edge computing (MEC)-assisted HAS by using edge cloud capabilities. Due to the non-deterministic polynomial-time hardness of the problem, we design a heuristic rate adaptation algorithm to jointly enhance the quality metrics of the competing clients. Our extension simulation results show that the proposed edge cloud-assisted rate adaptation algorithm outperforms the existing strategies under different client-side and server-side settings. Furthermore, we show that the proposed algorithm is promising under slow-moving and fast-moving environments.