MEC-Based Super-Resolution Enhanced Adaptive Video Streaming Optimization for Mobile Networks With Satellite Backhaul

Using satellite communications as backhaul links facilitates extending network coverage to unconnected areas. However, providing high-quality video streaming service via satellite backhaul is not economical. This paper presents SatSR, a mobile edge computing (MEC)-based super-resolution (SR)-enhanced adaptive on-demand video streaming system for mobile networks with satellite backhauls. Particularly, SR-based video quality enhancement is integrated into the video streaming process, so that low-quality videos with small sizes can be transmitted by satellite links and then enhanced to be high-quality. Meanwhile, SatSR offloads computation-intensive SR processing from user equipment (UE) to the MEC server to relieve UEs' computation burden and speed up the SR processing. Specifically, the framework and the operation process of SatSR are designed first. Then, to mitigate the impact of SR processing delay, a pipelined mechanism is proposed, which can coordinate the video transmission and SR-based enhancement efficiently. Furthermore, an SR scale factor adaptation algorithm based on deep reinforcement learning is proposed to cope with the fluctuation of communication links. Finally, a system prototype and a chunk-level simulator of SatSR are built, respectively. The experiments results validate that SatSR outperforms baselines significantly, including both the UE-based SR-enhancement video streaming scheme and the traditional bitrate adaptation based video streaming scheme.