Video sensors have become ubiquitous and are playing critical roles in a variety of important applications, e.g., surveillance, video conferencing, vision-assisted robots, etc. Many of the devices are constrained in onboard computing resources and communication capability, which forms a major bottleneck for efficient video analytics tasks. This project will develop effective video compression methods via deep learning, and the compressed data will then be sent to be processed at powerful edge computing platforms.