Abstract
Unmanned Aerial Vehicles (UAVs) or drones equipped with cameras are extensively used in different scenarios such as surveillance of hazardous locations, disaster response and crime fighting. The related video streaming/analytics requires real-time drone-to-Ground Control Station (GCS) communication and computation co-ordination for desired user Quality of Experience (QoE). In situations where the quality of the video can be affected by occlusions (e.g., image distortion, frame stalling) due to network bottlenecks, there is a need to dynamically make decisions on the computation offloading and networking protocols in order to properly handle the video data for real world application purposes. In this paper, we propose a novel function-centric computing approach that helps a user to perform drone video analytics to assess a wide-area scene to chart a plan of action. Our approach involves handling network impairments affecting the switching between high resolution/low resolution video capture, or change of camera direction for assessment of the scene effectively. It also features a novel video quality enhancing algorithm based on occlusion-detection that adapts to video impairments related to image distortion and frame stalling. Our experiment results from a realistic testbed show that our approach can efficiently choose the suitable networking protocols (i.e., TCP/HTTP, UDP/RTP, QUIC) and orchestrate both the camera control on the drone, and the computation off-loading of the video analytics over limited edge computing resources. The performance improvements for computation off-loading involving our video quality enhancing algorithm are shown for different network conditions in terms of occlusion rate and processing times.