Deep Reinforcement Learning With Communication Transformer for Adaptive Live Streaming in Wireless Edge Networks

Abstract

The emerging mobile edge computing (MEC) technology has been recently applied to improve the Quality of Experience (QoE) of network services, such as live video streaming. In this paper, we study an energy-aware adaptive live streaming scheme in wireless edge networks. In particular, we aim to design a joint uplink transmission and edge transcoding algorithm maximizing the video followers’ QoE, while minimizing the energy consumption of the video streamer. We formulate the problem as a Markov decision process (MDP), and propose a deep reinforcement learning (DRL) based framework, named SACCT, to determine the streamer’s encoding bitrate, the uploading power as well as the edge transcoding bitrates and frequency. We decompose the MDP problem into inter-frame and intra-frame problems to address the key design challenges that arise from continuous-discrete hybrid action space, time-varying state and action spaces, and unknown network variation. By doing so, SACCT integrates model-based optimization and model-free DRL to determine the intra-frame continuous resource allocation decisions and the inter-frame discrete bitrate adaptation decisions, respectively. To integrate both the numerical features (e.g., channel gain) and the categorical features (e.g., bitrate), we propose a communication Transformer (CT) as a backbone of SACCT by representing network states as communication tokens and running Transformers to model multi-scale dependencies. Extensive simulations manifest that compared with state-of-the-art approaches, SACCT can provide 128.23% (on average) extra reward. As such, by leveraging joint uplink adaption and edge transcoding, the proposed scheme enables an intelligent wireless network edge with QoE-assured and energy-aware live streaming services.