QoE‐based Framework to Optimize User Perceived Video Quality

L. Amour 1 S. Mushtaq 1 S. Souihi 1 A. Mellouk 1
1 CIR
LISSI - Laboratoire Images, Signaux et Systèmes Intelligents
Abstract : Video streaming has become a main contributor in an ever increasing Internet traffic, and meets the users expectation is a challenging task for both the Network service Provider (NsP) and Content service Provider (CsP). In this context, a new metric called: Quality of Experience (QoE) is evolved to measure the user satisfaction using video service, and it becomes a key driver for achieving the business goal of NsP and CsP. In this perspective, we have proposed a novel framework that considers the user QoE to adapt the video quality, named Optimized Quality of DASH (OQD). The objective of the proposed OQD framework is to optimize users experience, and maximize the bandwidth usage. A Machine Learning (ML) approach based on GRadient Boosting (GRB) method is implemented to predict the user QoE that considers three important network and application QoE Influence Factors (QoE IFs). We use the Reinforcement Learning (RL) approach to select the optimal video quality segment, which improves the user QoE. The performance of the proposed method is evaluated and compared against Greedy adaptive bit-rate method in terms of re-buffering, bandwidth utilization, average MOS, and standard deviation MOS. The results clearly show that proposed method performs well, as it considers the user's perceived video quality as a regulator to optimize the overall video delivery network.
Type de document :
Communication dans un congrès
Proc. Of the 42nd Annual IEEE Conference on Local Computer Networks, LCN 2017, 2017, Singapour, Singapore. pp.1-6, 2017
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Contributeur : Lab Lissi <>
Soumis le : vendredi 5 janvier 2018 - 17:44:22
Dernière modification le : mercredi 20 février 2019 - 10:44:38

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  • HAL Id : hal-01676576, version 1

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L. Amour, S. Mushtaq, S. Souihi, A. Mellouk. QoE‐based Framework to Optimize User Perceived Video Quality. Proc. Of the 42nd Annual IEEE Conference on Local Computer Networks, LCN 2017, 2017, Singapour, Singapore. pp.1-6, 2017. 〈hal-01676576〉

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