DAO: Dynamic Adaptive Offloading for Video Analytics

Offloading videos from end devices to edge or cloud servers is the key to enabling computation-intensive video analytics. To ensure the analytics accuracy at the server, the video quality for offloading must be configured based on the specific content and the available network bandwidth. While adaptive video streaming for user viewing has been widely studied, none of the existing works can guarantee the analytics accuracy at the server in bandwidth- and content-adaptive way. To fill in this gap, this paper presents DAO, a dynamic adaptive offloading framework for video analytics that jointly considers the dynamics of network bandwidth and video content. DAO is able to maximize the analytics accuracy at the server by adapting the video bitrate and resolution dynamically. In essence, we shift the context of adaptive video transport from traditional DASH systems to a new dynamic adaptive offloading framework tailored for video analytics. DAO is empowered by some new discoveries about the inherent relationship between analytics accuracy, video content, bitrate, and resolution, as well as by an optimization formulation to adapt the bitrate and resolution dynamically. Results from the real-world implementation of object detection tasks show that DAO's performance is close to the theoretical bound, achieving 20% bandwidth saving and 59% category-wise mAP improvement compared to conventional DASH schemes.