Adaptive Bitrate Streaming in Wireless Networks with Transcoding at Network Edge Using Deep Reinforcement Learning

Adaptive bitrate (ABR) streaming has been used in wireless networks to deal with the time-varying wireless channels. Traditionally, wireless video is fetched from remote Internet server. However, wireless video streaming from Internet server faces challenges such as congestion and long latency. ABR streaming and video transcoding at the radio access network (RAN) edge have shown the potential to overcome such problem and provided better video streaming service. In the paper, we consider joint computation and communication for ABR streaming based on mobile edge computing (MEC) under time-varying wireless channels. We propose a joint video transcoding and quality adaptation framework for ABR streaming by enabling RAN with computing capability. By modeling the wireless channel as a finite state Markov channel, we formulate the optimization problem as a stochastic optimization problem of joint computatio