In this paper, we investigate the latency minimization resource allocation for a multi-user augmented reality (AR) system based on mobile edge computing (MEC). First, we develop a novel data sharing model for the delay-sensitive AR tasks. Then, by integrating the partial offloading scheme into the task processing, we formulate a weighted-sum latency minimization problem to improve the quality of experience (QoE) for AR devices. Both the optimal task segmentation strategy and the optimal joint resource allocation are derived in closed-form. Finally, numerical results show that the proposed partial task offloading with data sharing scheme can achieve a better delay performance as compared against some benchmark schemes.
Data Offloading and Sharing for Latency Minimization in Augmented Reality Based on Mobile-Edge Computing
01.08.2018
159370 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch