Energy and Network Aware Mobile Augmented Reality
This dissertation has two main objectives -- solving power and latency issues in mobile augmented reality. For power, we showcase the power drain due to the two heaviest components -- simultaneous localization and mapping (SLAM) and deep convolutional neural networks (DNNs) and design solutions to reduce the power consumption on mobile devices. Our single-user solution is to use DNNs as needed, to detect new objects or recapture objects that significantly change in appearance, and otherwise depend on low-power object tracking. For multi-user solutions, we use peer-to-peer communications to exchange key information among devices, and finally assign roles to each of them -- primary or secondary. A primary device continuously tracks target objects and shares their information to slaves. Secondary devices do not need SLAM or DNN but leverage the shared information from the master and other lightweight methods to keep track of the objects with high precision, and thus significantly reduce power consumption. In addition, we can rotate the master functionality across participants in order to distribute energy expenditures among them and increase the longevity of the AR experience. For latency, we perform a first-of-its-kind measurement study on both public LTE and industry LTE testbed for two popular multi-user AR applications, yielding several insights such as: (1) The radio access network (RAN) accounts for a significant fraction of the end-to-end latency (31.2\%, or 3.9 s median); (2) AR network traffic is characterized by large intermittent spikes on a single uplink TCP connection, resulting in frequent TCP slow starts that can increase user-perceived latency; (3) Applying a common traffic management mechanism of cellular operators, QoS Class Identifiers (QCI), can help by reducing AR latency by 33\% but impacts non-AR users. Based on these insights, we propose AR solutions to intelligently adapt IP packet sizes and periodically provide information on uplink data availability, respectively. Our solutions help ramp up network performance, improving the end-to-end AR latency and goodput by ~40-70\%.