Data-driven QoE optimization techniques for multi-user wireless networks

The proliferation of heterogeneous data services and applications, with different communication requirements, has led to the design of Quality of Service (QoS) mechanisms to provide service differentiation and, possibly, performance guarantee to a range of classes of applications. In this paper, we propose a data-driven media delivery framework for the optimization of multi-user wireless networks that differs from the classic approaches in the following aspects. First, it goes beyond the QoS paradigm to embrace the Quality of Experience (QoE) approach, which discriminates data streams based on their actual content rather than just their class. Second, it applies cutting-edge cognitive science techniques to automatically learn data models and discover optimization strategies. To substantiate our argumentation, we discuss a couple of use cases regarding the transmission of multimedia content over a wireless link shared by users belonging to different QoE classes of service.