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Take off a Load: Load-Adjusted Video Quality Prediction and Measurement
KTH, School of Electrical Engineering (EES), Communication Networks. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. Waterford Institute of Technology,Ireland. (LCN)ORCID iD: 0000-0002-3912-1470
2015 (English)In: 13th IEEE International Conference on Dependable, Autonomic and Secure Computing: 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing,, IEEE Computer Society, 2015, 1887-1895 p.Conference paper (Refereed)
Abstract [en]

An algorithm for predicting the quality of video received by a client from a shared server is presented. A statistical model for this client-server system, in the presence of other clients, is proposed. Our contribution is that we explicitly account for the interfering clients, namely the load. Once the load on the system is understood, accurate client-server predictions are possible with an accuracy of 12.4% load adjusted normalized mean absolute error. We continue by showing that performance measurement is a challenging sub-problem in this scenario. Using the correct measure of prediction performance is crucial. Performance measurement is miss-leading, leading to potential over-confidence in the results, if the effect of the load is ignored. We show that previous predictors have over (and under) estimated the quality of their prediction performance by up to 50% in some cases, due to the use of an inappropriate measure. These predictors are not performing as well as stated for about 60% of the service levels predicted. In summary we achieve predictions which are ≈50% more accurate than previous work using just ≈2% of the data to achieve this performance gain –a significant reduction in computational complexity results. 

Place, publisher, year, edition, pages
IEEE Computer Society, 2015. 1887-1895 p.
Keyword [en]
Video-on-Demand, RTP Packet Rate, Prediction, Machine Learning, Linear Regression
National Category
Computer Systems Signal Processing
Research subject
Applied and Computational Mathematics
URN: urn:nbn:se:kth:diva-175434DOI: 10.1109/CIT/IUCC/DASC/PICOM.2015.280ISI: 000380514500284ScopusID: 2-s2.0-84964239596ISBN: 978-1-5090-0154-5OAI: diva2:860949
IEEE International Conference on Computer and Information, Oct 26-28, 2015, Liverpool, United Kingdom

Qc 20160212

Available from: 2015-10-14 Created: 2015-10-14 Last updated: 2016-09-20Bibliographically approved

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