Quality of Experience: modelling and application scenarios

The research activity has been focused on the concept of Quality of Experience (QoE), and specifically on QoE modelling and QoE application scenario. With regard to QoE modelling, a layered model for evaluating the QoE of multimedia services is proposed to address the problem of modelling un-interoperability. In the proposed model, each layer is devoted to a specific QoE domain (set of QoE influence factors (IFs)), so that the overall quality can be computed as a combination of all domains. To investigate the applicability of the proposed layered approach, a video streaming scenario is considered. The influence of system and interactivity IFs on the QoE perceived by the users when streaming video sequences on tablet devices are evaluated and combined for the estimation of the overall QoE. Additionally, the influence of device switching on the QoE perceived by users watching video contents is investigated. The aim is to demonstrate that the QoE is not only related to the video consumption on the single device, but it takes into consideration the overall experience of watching the whole video by switching between different devices. Finally, a first approach on QoE modelling for the Internet of Things (IoT) is discussed. The concept of Multimedia IoT (MIoT) is introduced in order to focus on the applications in which people are involved as the end-users of the multimedia content provided by the MIoT applications. Then, a layered QoE management framework is proposed, which aims at evaluating and controlling the contributions of each IF to estimate the overall QoE in MIoT applications. The second part of the thesis focuses on two different application scenarios in which the QoE is involved as a metric for system optimization and user profiling. The first application scenario investigates the role of the QoE in the collaboration between Internet Service Providers (ISPs) and Over The Top (OTT) services. Indeed, the provision of QoE is considered as the common objective for OTTs and ISPs because delivering good QoE helps to decrease the user churn and consequently to increase the revenue. A QoE-aware collaboration approach between ISP and OTT has been proposed, which is driven by the maximization of the revenue based on different factors, such as the user churn, pricing and marketing actions. In the second application scenario, the QoE is used as a priori measure of user’s satisfaction for profile characterization of the involved users appliances of a Smart Home Energy Management (SHEM) system. The QoE is considered as the degree of annoyance perceived when the starting time or the set temperature of appliances in a Smart Home is modified with respect to users’ preferences. A survey has been conducted to collect users’ preferences, which have been clustered in different profiles. The SHEM system runs a task scheduling model, which relies on two algorithms with the objective of scheduling the appliances’ operations, such as starting time and set temperature, so that a trade-off between energy expenses and annoyance perceived is achieved.