Predicting quality of experience for online video service provisioning View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-02-01

AUTHORS

Utku Bulkan, Tasos Dagiuklas

ABSTRACT

The expansion of the online video content continues in every area of the modern connected world and the need for measuring and predicting the Quality of Experience (QoE) for online video systems has never been this important. This paper has designed and developed a machine learning based methodology to derive QoE for online video systems. For this purpose, a platform has been developed where video content is unicasted to users so that objective video metrics are collected into a database. At the end of each video session, users are queried with a subjective survey about their experience. Both quantitative statistics and qualitative user survey information are used as training data to a variety of machine learning techniques including Artificial Neural Network (ANN), K-nearest Neighbours Algorithm (KNN) and Support Vector Machine (SVM) with a collection of cross-validation strategies. This methodology can efficiently answer the problem of predicting user experience for any online video service provider, while overcoming the problematic interpretation of subjective consumer experience in terms of quantitative system capacity metrics. More... »

PAGES

18787-18811

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11042-019-7164-9

DOI

http://dx.doi.org/10.1007/s11042-019-7164-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1111840676


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