Data–Driven Image Resolution and Uplink Power Control for Mobile Augmented Reality Applications

In the context of Mobile Augmented Reality, satisfying the challenging users’ requirements about Quality of Service and Quality of Experience is not an easy task due to the limited computing capabilities of mobile devices, and the rapid, free movement of users within the environment. To deal with these issues, graphical computations are typically offloaded from mobile devices to edge servers. While traditional offloading strategies rely on static optimization or heuristics, this work proposes a multi–input data–driven dynamic control of uplink power and image compression rate, introducing a Policy Broadcasting Deep Reinforcement Learning approach, based on the Deep Deterministic Policy Gradient algorithm. The proposed solution is aimed at matching the challenging Quality of Service constraints, in terms of maximum round–trip latency and minimum resolution accuracy, while minimizing the energy consumption. Simulations show the effectiveness and scalability of the proposed approach for real–time applications.