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The rapid development of multimedia and communication technology has resultedin an urgent need for high-quality video streaming. However, robust videostreaming under fluctuating network conditions and heterogeneous clientcomputing capabilities remains a challenge. In this paper, we consider anenhancement-enabled video streaming network under a time-varying wirelessnetwork and limited computation capacity. "Enhancement" means that the clientcan improve the quality of the downloaded video segments via image processingmodules. We aim to design a joint bitrate adaptation and client-sideenhancement algorithm toward maximizing the quality of experience (QoE). Weformulate the problem as a Markov decision process (MDP) and propose a deepreinforcement learning (DRL)-based framework, named ENAVS. As video streamingquality is mainly affected by video compression, we demonstrate that the videoenhancement algorithm outperforms the super-resolution algorithm in terms ofsignal-to-noise ratio and frames per second, suggesting a better solution forclient processing in video streaming. Ultimately, we implement ENAVS anddemonstrate extensive testbed results under real-world bandwidth traces andvideos. The simulation shows that ENAVS is capable of delivering 5%-14% moreQoE under the same bandwidth and computing power conditions as conventional ABRstreaming.

Enhancement or Super-Resolution: Learning-based Adaptive Video Streaming with Client-Side Video Processing