Collaborative Video Streaming With Super-Resolution in Multi-User MEC Networks

Abstract

The ever-increasing quality of experience (QoE) demand for video streaming has prompted the integration of video super-resolution and multi-access edge computing networks (MEC). With super-resolution, the low-resolution frames can be reconstructed into high-resolution ones by edge node and end device collaboratively, which is beneficial in improving QoE. However, the existing works focus on designing video streaming strategies in single-user scenarios, which cannot be applied to multi-user scenarios due to the resource contention among users, as well as the huge solution space of coupled bitrate selection and workload share between edge-end. To fill this gap, we propose a collaborative video streaming strategy with super-resolution in multi-user MEC networks, named Co-Video, to maximize the average QoE by making optimal bitrate selection and workload share. We first formulate the problem as an optimization problem towards maximum average QoE, where the QoE incorporates playback delay, video quality, and smoothness. Then, we transform the optimization problem into a partially observable Markov decision process (POMDP) and exploit the Co-Video strategy based on the multi-agent soft actor-critic (MASAC) algorithm. Specifically, Co-Video utilizes the branching actor network to converge to good policy stably. Finally, trace-driven simulations on real-world bandwidth traces demonstrate that Co-Video outperforms the state-of-the-art baselines.