Deep learning video analytic systems process live video feeds from multiple cameras with computer vision models deployed on edge or cloud. To optimize utility for these systems, which usually corresponds to query accuracy, efficient bandwidth management for the cameras competing for the fluctuating network resources is crucial. We propose DeepStream, a bandwidth efficient multi-camera video streaming system for deep learning video analytics. DeepStream addresses the challenge of limited and fluctuating bandwidth resources by offering several tailored solutions. We design a novel Regions of Interest detection (ROIDet) algorithm which can run in real time on resource constraint devices, such as Raspberry Pis, to remove spatial redundancy in video frames and reduce the amount of data to be transmitted. We also propose a content-aware bandwidth optimization framework and an Elastic Transmission Mechanism that exploits correlations among video contents. We implement DeepStream on Raspberry Pis and a desktop computer. Evaluations on real-world datasets show that DeepStream's ROIDet algorithm saves up to 54\% bandwidth with less than 1\% accuracy drop. Additionally,DeepStream improves utility by up to 23\% compared to baselines under the same bandwidth conditions.
@article{arxiv.2306.15129,
title = {DeepStream: Bandwidth Efficient Multi-Camera Video Streaming for Deep Learning Analytics},
author = {Hongpeng Guo and Beitong Tian and Zhe Yang and Bo Chen and Qian Zhou and Shengzhong Liu and Klara Nahrstedt and Claudiu Danilov},
journal= {arXiv preprint arXiv:2306.15129},
year = {2023}
}