Ricerc@Sapienza

Has now become more important than ever to guarantee an always present connectivity to users, especially in emergency scenarios. However, in case of a disaster, network infrastructures are often damaged, with consequent connectivity disruption, isolating users when are more in need for information and help. Drones may supply with a recovery network, thanks to their capabilities to provide network connectivity on the fly. However, users typically need special devices or applications to reach these networks, reducing their applicability and adoption.

In the last decade, the use of small-scale Unmanned Aerial Vehicles (UAVs) is increased considerably to support a wide range of tasks, such as vehicle tracking, object recognition, and land monitoring. A prerequisite of many of these systems is the construction of a comprehensive view of an area of interest. This paper proposes a small-scale UAV based system for real-time creation of incremental and geo-referenced mosaics of video streams acquired at low-altitude.

This paper deals with the adoption of Unmanned Aerial Vehicles (UAVs) as mobile Base Stations providing video streaming services within a cellular macro area. We devise a Q-learning based UAV flight planning algorithm aimed at improving the Quality of Experience (QoE) of video users. Specifically, the proposed algorithm, herein denoted as Q-SQUARE, leverages the well-established Q-learning algorithm by introducing a reward related to a key QoE metric that is the video segment delay.