Quality of Experience refers to the degree of delight or annoyance perceived by the user of an application or service. The purpose of this thesis is to leverage QoS measurements collected in the network to model and predict the QoE in the context of real-time communication applications. This case study focuses on video QoE and uses Cisco WebEx as real-time multiparty conference system. Experiments are conducted in different scenarios, using both active and passive measurements, and the QoS information extracted from the traffic captures are fed to machine learning algorithms to infer the QoS/QoE relationship and make QoE predictions starting from QoS features.