Intelli-AR Preloading: A Learning Approach to Proactive Hologram Transmissions in Mobile AR

Mobile augmented reality (AR), which integrates virtual objects (i.e., holographic contents) with 3-D real environments in real time, has been rapidly gaining popularity in the last five years. The delivery mechanisms of these holographic contents to mobile AR devices, however, are rarely investigated. To combat bandwidth limitations that preclude providing holographic contents to user devices on-demand, in this article, we propose the intelligent AR (Intelli-AR) preloading algorithm to improve transmission efficiency in the edge-assisted network, in which edge servers proactively transmit holographic contents to the devices. Without user devices’ future motion trajectories, the Intelli-AR preloading algorithm models the user devices’ motion trajectories as Markov decision process (MDP) and adaptively learns the optimal preloading policy. The Intelli-AR preloading is decomposed into two parts and separately deployed on the edge server and the user devices to reduce the computation complexity. The Intelli-AR solution improves the ratio of successful preloading by 11.52% compared to the best baseline in the practical data set when the users’ motion trajectories tend to be more random, and by 21.97% compared to the best baseline in the data set which is synthesized from a real-life mobile AR environment.