부산대학교 도서관

Live video streaming is increasingly popular for its rich content and real-time interactions, but its demand for bandwidth has put a heavy burden on backbone networks. To save bandwidth, recent studies have proposed neural-enhanced live video streaming that deploys deep neural networks (DNNs) for video super-resolution (VSR) on end devices or nearby edge devices to enhance video quality by taking low-resolution frames as input and producing high-resolution output frames. In this solution, the high computational demands of high-quality VSR DNNs make them difficult to support on single end or edge device, necessitating the use of distributed resources in edge facilities. However, the distributed deployment of high-quality VSR DNNs for low-latency inference remains challenging due to the inherent data dependencies of VSR DNNs and the heterogeneity and dynamics of edge facilities. In this paper, we present WAEVSR, a novel collaborative neural-enhanced live video super-resolution system that enables effective leverage of distributed resources to maximize the latency-bounded quality in wide-area MEC environments. WAEVSR consists of two key components: 1) It deploys a parallel-friendly video super-resolution DNN among edge devices, 2) with an inference controller based on the variable-size sliding window to balance the latency and quality of distributed inference in the heterogeneous and dynamics MEC environment. Prototype-based evaluation shows that WAEVSR can achieve 2.5 × lower end-to-end latency than traditional super-resolution serving with a 0.01 drop in SSIM score. The case study also demonstrates its higher stability on latency than vanilla distributed MEC deployment.