Adaptive Video Streaming in Multi-Tier Computing Networks: Joint Edge Transcoding and Client Enhancement

With the advancement of multimedia technology and wireless networks, there is a growing demand for high-quality video streaming. Delivering stable video streaming in extremely dynamic wireless networks, nevertheless, is still an open problem. Recent developments in client computing and mobile edge computing (MEC) technologies have both shown promise in enhancing the adaptive bitrate (ABR) streaming services. In this paper, we consider a video streaming system in multi-tier computing networks, enabled by joint edge-side video transcoding and client-side video enhancement. By “enhancement,” we mean that the client improves the video chunk quality via client-side image processing modules. In particular, we aim to design a joint bitrate adaptation, edge transcoding, and client image-processing algorithm, maximizing the quality of experience (QoE) of streaming services. The majority of the prior art has concentrated on super-resolution-enabled video streaming. Contrarily, we show that the video enhancement method outperforms the super-resolution approach in terms of signal-to-noise ratio and frames per second, implying a superior alternative for client-side processing in ABR streaming. We formulate the problem as an event-triggered Markov decision process (E-MDP), and propose a deep reinforcement learning (DRL)-based framework, named EDTEA. To deal with the delayed feedback induced by multi-tier computing, the entropy and the expected re-buffering terms are introduced to the objective and the reward, respectively. Extensive simulations based on real-world videos and bandwidth traces manifest that compared with state-of-the-art approaches, EDTEA provides
$10.4\%\sim 78.4\%$
extra QoE while reducing re-buffering time by
$85.5\%\sim 91.7\%$
.