QoE-oriented Adaptive Video Streaming with Edge-Client Collaborative Super-Resolution

In mobile video streaming, the ever-increasing user expectations for Quality of Experience (QoE) have prompted the integration of video super-resolution and adaptive bitrate techniques on either the mobile device or the edge server. By reconstructing high-resolution frames from low-resolution frames that have been downloaded, both high video quality and a short rebuffer time can be enjoyed. However, the exiting methods merely leverage the computing resources of the edge server or mobile device, leaving significant room for further QoE improvement. In this paper, we present an adaptive Video Streaming system with Edge-Client collaborative Super-resolution, named VSECS, to enhance users' QoE by simultaneously utilizing the computing resources of both the edge server and mobile device to reconstruct high-resolution frames collaboratively. First, we deploy a large-scale super-resolution model on the edge server and a lightweight model on the mobile device. Then, we exploit the Asynchronous Advantage Actor-Critic (A3C) algorithm to make decisions regarding the download resolution, the reconstructed target resolution, and the workload share of the mobile device, considering the network bandwidth, computing resources, and reconstruction complexity of video tiles. Furthermore, we utilize the branching actor network to enable the agent to converge to good policy stably. Trace-driven simulations on real-world bandwidth traces demonstrate that our approach can improve QoE by up to 10% compared to the state-of-the-art video streaming solutions.