  The rapid development of multimedia and communication technology has resulted
in an urgent need for high-quality video streaming. However, robust video
streaming under fluctuating network conditions and heterogeneous client
computing capabilities remains a challenge. In this paper, we consider an
enhancement-enabled video streaming network under a time-varying wireless
network and limited computation capacity. "Enhancement" means that the client
can improve the quality of the downloaded video segments via image processing
modules. We aim to design a joint bitrate adaptation and client-side
enhancement algorithm toward maximizing the quality of experience (QoE). We
formulate the problem as a Markov decision process (MDP) and propose a deep
reinforcement learning (DRL)-based framework, named ENAVS. As video streaming
quality is mainly affected by video compression, we demonstrate that the video
enhancement algorithm outperforms the super-resolution algorithm in terms of
signal-to-noise ratio and frames per second, suggesting a better solution for
client processing in video streaming. Ultimately, we implement ENAVS and
demonstrate extensive testbed results under real-world bandwidth traces and
videos. The simulation shows that ENAVS is capable of delivering 5%-14% more
QoE under the same bandwidth and computing power conditions as conventional ABR
streaming.
