  The computational capabilities of recent mobile devices enable the processing
of natural features for Augmented Reality (AR), but the scalability is still
limited by the devices' computation power and available resources. In this
paper, we propose EdgeXAR, a mobile AR framework that utilizes the advantages
of edge computing through task offloading to support flexible camera-based AR
interaction. We propose a hybrid tracking system for mobile devices that
provides lightweight tracking with 6 Degrees of Freedom and hides the
offloading latency from users' perception. A practical, reliable and unreliable
communication mechanism is used to achieve fast response and consistency of
crucial information. We also propose a multi-object image retrieval pipeline
that executes fast and accurate image recognition tasks on the cloud and edge
servers. Extensive experiments are carried out to evaluate the performance of
EdgeXAR by building mobile AR Apps upon it. Regarding the Quality of Experience
(QoE), the mobile AR Apps powered by EdgeXAR framework run on average at the
speed of 30 frames per second with precise tracking of only 1~2 pixel errors
and accurate image recognition of at least 97% accuracy. As compared to
Vuforia, one of the leading commercial AR frameworks, EdgeXAR transmits 87%
less data while providing a stable 30 FPS performance and reducing the
offloading latency by 50 to 70% depending on the transmission medium. Our work
facilitates the large-scale deployment of AR as the next generation of
ubiquitous interfaces.
