An Intelligent Video Processing Architecture for Edge-cloud Video Streaming

This work proposes an intelligent video processing architecture for bandwidth-efficient edge-cloud video streaming. On receiving the bandwidth-saving low-quality video streaming in compressed format, the proposed architecture can perform direct DNN-based video enhancement, e.g., super-resolution and motion-compensated frame interpolation (MCFI), on streams. By utilizing the metadata motion vectors and residuals extracted from the encoded video, our workflow will significantly eliminate the unnecessary pixels being processed by the video-enhancing DNNs, and greatly promote the execution efficiency. The evaluation results on popular datasets show that our architecture can reduce the edge-side processing latency of video-enhancing DNNs by 90% compared to the traditional flow while producing accurate and high-quality videos on edge.