Predicting quality of experience in multimedia streaming

Menkovski, Vlado; Oredope, Adetola; Liotta, Antonio; S�nchez, Antonio Cuadra

Abstract

Measuring and predicting the user's Quality of Experience (QoE) of a multimedia stream is the first step towards improving and optimizing the provision of mobile streaming services. This enables us to better understand how Quality of Service (QoS) parameters affect service quality, as it is actually perceived by the end user. Over the last years this goal has been pursued by means of subjective tests and through the analysis of the user's feedback. Existing statistical techniques have lead to poor accuracy (order of 70%) and inability to evolve prediction models with the system's dynamics. In this paper, we propose a novel approach for building accurate and adaptive QoE prediction models using Machine Learning classification algorithms, trained on subjective test data. These models can be used for real-time prediction of QoE and can be efficiently integrated into online learning systems that can adapt the models according to changes in the environment. Providing high accuracy of above 90%, the classification algorithms become an indispensible component of a mobile multimedia QoE management system.

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: MoMM '09: 7th International Conference on Advances in Mobile Computing and Multimedia
• Start Date: Dec 14, 2009
• End Date: Dec 16, 2009
• Publication Date: 2009
• Deposit Date: Dec 5, 2019
• Publisher: Association for Computing Machinery (ACM)
• Pages: 52-59
• Book Title: MoMM '09 Proceedings of the 7th International Conference on Advances in Mobile Computing and Multimedia
• ISBN: 978-1-60558-659-5
• DOI: https://doi.org/10.1145/1821748.1821766
• Keywords: Quality of Experience (QoE); multimedia streaming; prediction models
• Public URL: http://researchrepository.napier.ac.uk/Output/1995860