Predicting quality of experience for online video service provisioning

Journal article

Bulkan, U and Dagiuklas, A. (2019). Predicting quality of experience for online video service provisioning. Multimedia Tools and Applications.
AuthorsBulkan, U and Dagiuklas, A.
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.

KeywordsQuality of Experience; Online Video Services; QoE Modelling; HTTP Streaming
Year2019
JournalMultimedia Tools and Applications
PublisherSpringer
ISSN1380-7501
Digital Object Identifier (DOI)doi:10.1007/s11042-019-7164-9
Publication dates
Print01 Feb 2019
Publication process dates
Deposited25 Feb 2019
Accepted03 Jan 2019
Publisher's version
License
CC BY 4.0
File Access Level
Open
Page range1-25
Permalink -

https://openresearch.lsbu.ac.uk/item/867w9

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