Abstract

The advent of Mobile Vision Analytics (MVA) is transforming applications from intelligent surveillance to immersive Augmented Reality (AR) in next-generation wireless networks. These systems, however, are constrained by the limited computation and energy of user devices and the stringent latency requirements of real-time video analytics. Edge computing provides a promising solution but introduces new challenges in wireless resource allocation, distributed task orchestration, and multi-tenant GPU scheduling. This thesis presents a comprehensive study on the modeling, design, and optimization of edge-assisted MVA systems.

First, we develop mathematical frameworks that capture the end-to-end pipeline of edge-assisted video analytics, integrating wireless transmission, task offloading, and concurrent DNN execution. Using queuing theory and stochastic modeling, we quantify trade-offs among latency, accuracy, energy, and frame drop rates. Next, we design load-aware orchestrators that balance workloads across multi