Latency-aware Hybrid Edge Cloud Framework for Mobile Augmented Reality Applications

Mobile Augmented Reality (AR) has become a reality thanks to improvements in mobile hardware. Still, mobile AR lags behind its desktop counterpart in both latency and performance. Simple offloading to external computers has been attempted, but is not practical due to high communication latency and adverse user experience. In this paper, we propose a novel Mobile Edge Computing framework for Augmented Reality applications (MEC-AR). MEC-AR is designed to take advantage of 5G cellular networks and make optimized computation-offloading decisions in a multi-tiered hierarchy. A three-layered architecture involving the end user, the mobile edge, and finally the cloud is envisioned. In the context of MEC resource management, we cast a Mixed Integer Linear Program (MILP) that aims at finding an efficient application placement on the MEC-AR layers to minimize the network latency. We evaluate the performance of our proposed MEC-AR framework by conducting extensive experimental analysis using images taken around Rutgers University. Simulation results coupled with real-time experiments on a small-scale MEC testbed show that our hierarchical computation mechanism improves the performance of mobile AR applications in terms of both energy consumption and network latency.