Estimations and remedies for quality of experience in multimedia streaming

V. Menkovski, G. Exarchakos, A. Liotta, A. Cuadra Sánchez

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

8 Citations (Scopus)
99 Downloads (Pure)

Abstract

Managing multimedia network services in a User-centric manner provides for more delivered quality to the users, whilst maintaining a limited footprint on the network resources. For efficient User-centric management it is imperative to have a precise metric for perceived quality. Quality of Experience (QoE) is such a metric, which captures many different aspects that compose the perception of quality. The drawback of using QoE is that due to its subjectiveness, accurate measurement necessitates execution of cumbersome subjective studies. In this work we propose a method that uses Machine Learning techniques to build QoE prediction models based on limited subjective data. Using those models we have developed an algorithm that generates the remedies for improving the QoE of observed multimedia stream. Selecting the optimal remedy is done by comparing the costs in resources associated to each of them. Coupling the QoE estimation and calculation of remedies produces a tool for effective implementation of a User-centric management loop for multimedia streaming services.
Original languageEnglish
Title of host publicationProceedings Third International Conference on Advances in Human-Oriented and Personalized Mechanisms, Technologies and Services (CENTRIC 2010, August 22-27, 2010, Nice, France)
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages11-15
ISBN (Print)978-1-4244-7778-4
DOIs
Publication statusPublished - 2010

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