DARE: Dynamic Adaptive Mobile Augmented Reality with Edge Computing
Qiang Liu, Tao Han · 2018
Mobile augmented reality (MAR) is a killer application of mobile edge computing because of its high computation demand and stringent latency requirement. Since edge networks and computing resources are highly dynamic, handling such dynamics is essential for providing high-quality MAR services. In this paper, we design a new network protocol named DARE (dynamic adaptive AR over the edge) that enables mobile users to dynamically change their AR configurations according to wireless channel conditions and computation workloads in edge servers. The dynamic configuration adaptations reduce the service latency of MAR users and maximize the quality of augmentation (QoA) under varying network conditions and computation workloads. Considering the video frame size and computation model, i.e., object detection algorithms, as two key parameters in adapting the AR configuration, we develop analytical models to characterize the impact of these parameters on QoA and the service latency. Then, we design optimization mechanisms on both the edge server and AR devices to guide the AR configuration adaptation and server computation resource allocation. The performance of the DARE protocol is validated through a small-scale testbed implementation.