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Building a Large Dataset for Model-based QoE Prediction in the Mobile Environment

L. Amour S. Souihi 1 S. Hoceini 1 A. Mellouk 1
1 CIR
LISSI - Laboratoire Images, Signaux et Systèmes Intelligents
Abstract : The tremendous growth in video services , specially in the context of mobile usage , creates new challenges for network service providers : How to enhance the user ' s Quality of Experience (QoE) in dynamic wireless networks (UMTS , HSPA , LTE/LTE - A) . The network operators use different methods to predict the user 's QoE . Generally to predict the user 's QoE , methods are based on collecting subjective QoE scores given by users . Basically , these approaches need a large dataset to predict a good perceived quality of the service . In this paper , we setup an experimental test based on crowdsourcing approach and we build a large dataset in order to predict the user ' s QoE in mobile environment in term of Mean Opinion Score (MOS) . The main objective of this study is to measure the individual / global impact of QoE Influence Factors (QoE IFs) in a real environment . Based on the collective dataset , we perform 5 testing scenarios to compare 2 estimation methods (SVM and ANFIS) to study the impact of the number of the considered parameters on the estimation . It became clear that using more parameters without any weighing mechanisms can produce bad results .
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https://hal.archives-ouvertes.fr/hal-01567527
Contributor : Yacine Amirat <>
Submitted on : Monday, July 24, 2017 - 9:29:12 AM
Last modification on : Friday, October 4, 2019 - 1:28:27 AM

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  • HAL Id : hal-01567527, version 1

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L. Amour, S. Souihi, S. Hoceini, A. Mellouk. Building a Large Dataset for Model-based QoE Prediction in the Mobile Environment. Proc of the 18th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, MSWIM 2015, 2015, Cancun, Mexico. pp.313-317. ⟨hal-01567527⟩

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