Quality-Aware Video Offloading in Mobile Edge Computing: A Data-driven Two-stage Stochastic Optimization
Weibin Ma, Lena Mashayekhy · 2021
Most camera-based mobile devices require ultra low-latency video analytics such as object detection and action recognition. These devices face severe resource constraints, and thus, video offloading to Mobile Edge Computing (MEC) seems a reasonable solution. However, MEC is facing several key challenges-especially due to uncertainties caused by dynamic device mobility-to provide efficient video offloading solutions that enable both maximum performance for video analytics and minimum latency. In this paper, we study the Video Offloading Problem (VOP) in MEC in detail to address these challenges. We formulate VOP as a Two-stage Stochastic Program, called TSP-VOP, to model the uncertainties in the environment. We propose a novel clustering-based Sample Average Approximation to effectively solve TSP- VOP in uncertain dynamic environments, while satisfying the required latency. We perform extensive experiments to validate the effectiveness of our proposed algorithm.