Rendering Super Resolution Video Streaming Efficiently with in-Network Computing

Emerging live video streaming applications, e.g., Ultra High Definition videos and interactive video streaming, have put forward new demands for ultra-high bandwidth and reduced delay to match the desired quality of experience. Since current on-device Super-Resolution (SR) approaches are hindered by the limited end-device capabilities, we are motivated to take advantage of Mobile Edge Computing (MEC) and Computing in the Network technologies, such that SR videos can be processed on a more powerful infrastructure by integrating the resources from end-devices through the edge and all the way to the cloud. However, the integration of SR and MEC is non-trivial due to the challenges introduced by the features of SR tasks, and the heterogeneous nature of MEC resources. In this paper, we endeavor to explore and solve these challenges by presenting AVSA, which renders SR live video streaming efficiently with in-network computing. AVSA can adaptively allocate SR work-loads under heterogeneous resources and yield a cost-effective workload allocation with theoretical performance guarantees. Simulation results show that, compared with the state-of-the-art methods, our method achieves up to 13.06× speedup in terms of end-to-end latency.