Poster Abstract: Quality of Experience Aware Collaborative Augment Reality system (Q-CARs)

Achieving high QoE for AR applications on a mobile device is challenging because the required advanced computer vision and machine learning algorithms are computationally intensive by nature. Hence, some task for AR applications must be offloaded to more powerful remote servers. Offloading for mobile applications remains an active area of research. In the context of mobile AR applications which requires continuous processing, offloading algorithms must be applied with care due to the high wireless network latencies. In fact, there are only a few mobile AR QoE frameworks to take wireless network conditions into consideration, and none of them studied the relationship between mobile AR QoE and wireless channel status that impacts each other along the time scale. In this project, we propose Q-CARS system, which will provide real test results that facilitate the design of the wireless network that enhances the support of QoE in AR systems.