  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.
