Edge-assisted video analytics is gaining momentum. In this work, we tackle an important problem to compress video content live streamed from the device to the edge without scarifying accuracy and timeliness of its video analytics. We find that on-device processing can be tuned over a larger configuration space for more video compression, which was largely overlooked. Inspired by our pilot study, we design VPPlus to fulfill the potentials to compress the video as much as we can, while preserving analytical accuracy. VPPlus incorporates two core modules – offline profiling and online adaptation – to generate proper feedback automatically and quickly to tune on-device processing. We validate the effectiveness and efficiency of VPPlususing five object detection tasks over two popular datasets; VPPlus outperforms the state-of-art approaches in almost all the cases.