Take off a Load: Load-Adjusted Video Quality Prediction and Measurement

Ruairí de Fréin

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

    Abstract

    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.
    Original languageEnglish
    Title of host publication2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing
    Pages1-10
    DOIs
    Publication statusPublished - 2015

    Publication series

    Name2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing

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