  With the development of artificial intelligence (AI) techniques and the
increasing popularity of camera-equipped devices, many edge video analytics
applications are emerging, calling for the deployment of computation-intensive
AI models at the network edge. Edge inference is a promising solution to move
the computation-intensive workloads from low-end devices to a powerful edge
server for video analytics, but the device-server communications will remain a
bottleneck due to the limited bandwidth. This paper proposes a task-oriented
communication framework for edge video analytics, where multiple devices
collect the visual sensory data and transmit the informative features to an
edge server for processing. To enable low-latency inference, this framework
removes video redundancy in spatial and temporal domains and transmits minimal
information that is essential for the downstream task, rather than
reconstructing the videos at the edge server. Specifically, it extracts compact
task-relevant features based on the deterministic information bottleneck (IB)
principle, which characterizes a tradeoff between the informativeness of the
features and the communication cost. As the features of consecutive frames are
temporally correlated, we propose a temporal entropy model (TEM) to reduce the
bitrate by taking the previous features as side information in feature
encoding. To further improve the inference performance, we build a
spatial-temporal fusion module at the server to integrate features of the
current and previous frames for joint inference. Extensive experiments on video
analytics tasks evidence that the proposed framework effectively encodes
task-relevant information of video data and achieves a better rate-performance
tradeoff than existing methods.
