  High-definition (HD) cameras for surveillance and road traffic have
experienced tremendous growth, demanding intensive computation resources for
real-time analytics. Recently, offloading frames from the front-end device to
the back-end edge server has shown great promise. In multi-stream competitive
environments, efficient bandwidth management and proper scheduling are crucial
to ensure both high inference accuracy and high throughput. To achieve this
goal, we propose BiSwift, a bi-level framework that scales the concurrent
real-time video analytics by a novel adaptive hybrid codec integrated with
multi-level pipelines, and a global bandwidth controller for multiple video
streams. The lower-level front-back-end collaborative mechanism (called
adaptive hybrid codec) locally optimizes the accuracy and accelerates
end-to-end video analytics for a single stream. The upper-level scheduler aims
to accuracy fairness among multiple streams via the global bandwidth
controller. The evaluation of BiSwift shows that BiSwift is able to real-time
object detection on 9 streams with an edge device only equipped with an NVIDIA
RTX3070 (8G) GPU. BiSwift improves 10%$\sim$21% accuracy and presents
1.2$\sim$9$\times$ throughput compared with the state-of-the-art video
analytics pipelines.
