Empirical study based on machine learning approach to assess the QoS/QoE correlation

Muhammad Sajid Mushtaq (1) , Brice Augustin (1) , Abdelhamid Mellouk (1)

1 LISSI - Laboratoire Images, Signaux et Systèmes Intelligents

The appearance of new emerging multimedia services have created new challenges for cloud service providers, which have to react quickly to end-users experience and offer a better Quality of Service (QoS). Cloud service providers should use such an intelligent system that can classify, analyze, and adapt to the collected information in an efficient way to satisfy end-users' experience. This paper investigates how different factors contributing the Quality of Experience (QoE), in the context of video streaming delivery over cloud networks. Important parameters which influence the QoE are: network parameters, characteristics of videos, terminal characteristics and types of users' profiles. We describe different methods that are often used to collect QoE datasets in the form of a Mean Opinion Score (MOS). Machine Learning (ML) methods are then used to classify a preliminary QoE dataset collected using these methods. We evaluate six classifiers and determine the most suitable one for the task of QoS/QoE correlation.

Mots clés: Multimedia Services; QoS; QoE

Domaines: Informatique [cs]

Muhammad Sajid Mushtaq, Brice Augustin, Abdelhamid Mellouk. Empirical study based on machine learning approach to assess the QoS/QoE correlation. 17th European Conference on Networks and Optical Communications (NOC), 2012, Jun 2012, Barcelona, Spain. pp.1,7, ⟨10.1109/NOC.2012.6249939⟩. ⟨hal-00917062⟩