HyperRTV: Neural-Enhanced Adaptive Real-Time Video Streaming Based on Terminal-Edge Collaboration

Baijun Chen, Daheng Yin, Lifei Teng, Fang Dong

Abstract: Video has become the primary source of Internet traffic due to the advance of streaming media technology and the surge in user demand for real-time video streaming applications. In this case, neural-enhanced video streaming and mobile edge computing are proposed to improve video content quality on nearby MEC (Mobile Edge Computing) devices and to maintain low interaction delay under limited bandwidth. In order to maximize the architectural advantages brought by MEC, we designed HyperRTV, a terminal-edge collaborative real-time video transmission system with neural-enhanced streaming. HyperRTV consists of three key components: 1) a video super-resolution structure that uses hardware-accelerated DNNs to satisfy latency limits; 2) an adaptive bitrate controller that can dynamically adjust the bitrate under various network conditions; 3) a heuristic task offloading strategy based on the ant colony algorithm to further meet the heterogeneity and dynamics of the MEC environment. Experiments show that HyperRTV decreases bandwidth consumption by 46% on average and achieves a 48%-65% reduction in latency, consistently maintaining better visual quality and higher stability in unpredictable networks. Our task offloading strategy can reduce the execution and transfer time by 16.1%-19.5% in a MEC environment with multi-task requests.