Edge Computing Enabled Mobile Augmented Reality with Imperfect Channel Knowledge

Abstract:
Mobile edge computing (MEC) is one of the promising solutions to enable augment reality (AR) by processing computational-intensive tasks within short latency constraint. However, the quality of user experience of MEC-enabled AR will significantly degrade in mobile scenarios, as it is difficult to obtain accurate channel state information (CSI) to make optimal offloading decision. In this paper, we propose an online offloading algorithm based on Lyapunov optimization to dynamically optimize the selections of the transmission rate and edge server without prior CSI. The algorithm makes a tradeoff between the reliability and latency in the MEC-enabled AR system. The numerical results show that the proposed algorithm outperforms the scheme with outdated CSI and is particularly applicable to the mobile scenarios for AR applications.