Ricerc@Sapienza

This paper investigates the benefits of flexible
resource allocation when performing HTTP-based Adaptive
Streaming (HAS) across cellular systems such as Long Term
Evolution (LTE). To guarantee video fluidity in the presence of
fluctuations of the instantaneous video source rate and channel
capacity, we consider a HAS based proxy video manager and
resource controller located at the cellular base station. Based
on the channel quality observed by mobile clients, the manager
allocates the wireless bandwidth to mobile clients for transmitting

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.

In this paper, we apply the recent MPEG-DASH SAND standard for streaming-aware networking elements to develop and investigate an integrated mobile network bandwidth management and resource allocation mechanism. It is used to achieve high quality of experience of the mobile streaming users while jointly assuring high revenue operations by the network operator and by the system's video service providers.

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.