Towards video streaming analysis and sharing for multi-device interaction with lightweight DNNs

Multi-device interaction has attracted a growing interest in both mobile communication industry and mobile computing research community as mobile devices enabled social media and social networking continue to blossom. However, due to the stringent low latency requirements and the complexity and intensity of computation, implementing efficient multi-device interaction for real-time video streaming analysis and sharing is still in its infancy. Unlike previous approaches that rely on high network bandwidth and high availability of cloud center with GPUs to support intensive computations for multi-device interaction and for improving the service experience, we propose MIRSA, a novel edge centric multi-device interaction framework with a lightweight end-to-end DNN for on-device visual odometry (VO) streaming analysis by leveraging edge computing optimizations with three main contributions. First, we design MIRSA to migrate computations from the cloud to the device side, reducing the high overhead for large transmission of video streaming while alleviating the server load of the cloud. Second, we design a lightweight VO network by utilizing temporal shift module to support on-device pose estimation. Third, we provide on-device resource-aware scheduling algorithm to optimize the task allocation. Extensive experiments show MIRSA provides real-time high quality pose estimation as an interactive service and outperforms baseline methods.