Reine: Reinspection Necessity-Based Video Collaborative Edge Caching in Smart Factory

In intelligent factories, multiple industrial cameras capture continuous videos and upload them to edge nodes for automatic preinspection. For reliability, video chunks with low preinspection accuracy must be delivered to quality inspectors for manual reinspection. Video edge caching is an urgent technology for fast and efficient manual reinspection. However, time-varying and constrained industrial network conditions cannot meet the increasing demand for video-oriented quality reinspection. This article proposes a reinspection necessity-based video collaborative edge caching method, called Reine, utilizing video superresolution (VSR) for industrial quality reinspection. First, a video edge collaborative caching framework is established for industrial quality reinspection. Second, a reinspection necessity metric is designed, and a video edge caching strategy based on VSR benefit is proposed for edge nodes. Third, an NP-hard integer nonlinear programming problem is formulated for collaborative video caching and adaptive bitrate decisions. Simulation validates Reine outperforms the state-of-the-art video edge caching methods.