Sensitivity-Aware Spatial Quality Adaptation for Live Video Analytics

Abstract

To address the conflict between the limited network bandwidth and high DNN inference accuracy, live video analytics desires a bandwidth-efficient streaming approach. To this end, more and more works study spatially variable quality streaming where high quality is only used for important regions. The key challenges are to accurately identify the important regions and select the right qualities for them to maximize accuracy. Existing approaches use either cheap analytics models or low-quality videos to locate important regions, and employ heuristic rules to make quality decisions, which struggle to address the above challenges. Our key insight is that the region’s accuracy “sensitivity” obtained by running the expensive DNN model on the high-quality video provides a reliable indication of the region’s importance and allows to allocate the available bandwidth optimally over regions by explicitly maximizing the frame accuracy. This work presents a sensitivity-aware algorithm Orchestra, which incorporates sensitivity into the design of spatial quality adaptation, including video zoning and quality selection. The design of Orchestra entails three main contributions: a feasible way of sensitivity estimation, sensitivity-aware zoning, and deduction-based accuracy estimation. Extensive experiments over realistic videos and network traces show that Orchestra improves accuracy by upto 14.1% with comparable bandwidth usage or reduces bandwidth usage by upto 44.2% while maintaining higher accuracy compared to baselines.