Ricerc@Sapienza

We develop and study adaptive rate scheduling mechanisms over cellular wireless networks, as used for the unicast provision of video streams to client mobiles at variable quality of experience (QoE) levels. Under service type I, mobile users receive video streams at a QoE level that is not lower than a specified value. In addition, mobile users that experience sufficiently high communications channel quality levels, may be provided video streams at higher video quality levels.

In this paper, we address the problem of devising a Quality of Experience (QoE) aware flight plan for UAV mounted Base Station within heterogeneous networks. Specifically, we propose a QoE aware flight planning algorithm leveraging the well established Q-learning approach and introducing a reward related to relevant QoE metrics. Numerical simulation results show the effectiveness of the QoE-aware learning algorithm to devise a flight path such that the UAVs mounted Base Station actually improves the QoE of the heterogeneous network users.

Today, an ever-increasing number of diseases requiring a surgical solution are treated by means of minimally invasive procedures [1]. When possible, these procedures are suggested instead of traditional approaches because they decrease both the surgical risk and postsurgery hospitalization. The growing technological advances in terms of imaging techniques (i.e., visualization of organs and tissues) and handling of the minimally invasive instruments (e.g., endoscopes) play a key role in improving the qualitative aspects of the interventions [2].