ROIAdaptor: Adaptive Task Offloading of ROI-Encoded Videos for Edge Video Analytics

Real-time analytics on video data demands intensive computation resources and high bandwidth consumption. Edge computing enables us to offload resource-intensive analytics tasks to nearby edge servers, effectively reducing the extended latency. Numerous studies have applied ROI encoding technology to decrease video data size, thereby decreasing transmission latency. However, ROI encoding will reduce inference accuracy and indirectly affect inference latency. Existing works have ignored these impacts, leading to significant performance degradation. In this paper, we first identified the necessity of considering video QP settings when offloading, and investigated the impacts of changing QP settings on accuracy and latency to demonstrate that adaptive offloading based on QP settings can improve the efficiency of video analytics. We formulate the problem of minimizing average latency to meet real-time requirements under accuracy constraints, and propose an online offloading algorithm called ROIAdaptor, based on a contextual multi-armed bandit method. Our algorithm is developed based on LinUCB, a contextual multi-armed bandit method, and operates online with historical information, achieving a provable performance bound. Simulation results show that ROIAdaptor can reduce the overall latency by an average of 35.7% and meet the requirements for real-time video analysis, with virtually no loss in accuracy.