Adaptive Overlap Padding and Resolution Selection for Frame Split-based Edge Video Analytics.

For providing accurate and fast on-device high-resolution video analytics, edge-assisted methods are widely proposed by using lower-resolution frames and splitting them with overlap padding. The use of low-resolution frames can significantly reduce the computational workload of video analytics. Dividing a frame into multiple overlapping parts simultaneously improves parallelism and maintains processing accuracy. However, accuracy and latency serve as a pair of tradeoff metrics, and prioritizing one to optimize overlap padding or resolution selection will compromise the other metric. Fortunately, we have discovered that in real-world video analytics scenarios with varying object sizes, it is not necessary to simultaneously achieve a high overlap padding size and high resolution. Hence, how to set appropriate overlap padding size and resolution to strike a balance between accuracy and latency in practical scenarios is a nontrivial and intractable problem. To this end, we propose an online learning-based method to achieve adaptive overlap padding and resolution selection, called APR. We model the problem as an integer programming and propose a Muli-armed bandit (MAB) theory-based algorithm to solve it. We discretize the continuum overlap padding size into a finite set to narrow explore space and set the frame split strategy as context information to achieve fast convergence. Theoretical analysis reveals APR achieves sub-linear regret. Extensive experimental results show APR outperforms the benchmark methods, achieving up to 2.06 × speedup in terms of latency and 0.18× increase in accuracy.