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HAS QoE prediction based on dynamic video features with data mining in LTE network

LTE网络中基于动态视频特征数据挖掘的适应流媒体用户体验预测

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Abstract

Evaluation of HTTP adaptive streaming (HAS) quality of experience (QoE) over LTE network is a challenging topic because of multi-segment and multi-rate features of dynamic video sequences. Different from the traditional QoE evaluation methods based on network parameters, this paper proposes the HAS QoE prediction methods based on its dynamic video segment features with data mining. Considering the application requirement of the trade-off between accuracy and complexity, two sets of methodologies are designed to evaluate the HAS QoE including regression and classification. In regression method, we propose the evolved PSNR (ePSNR) model using differential peak signal to noise ratio (dPSNR) statistics as the segment features to evaluate HAS QoE. In classification method, we propose the improved weighted k-nearest neighbors (WkNN) by using dynamic weighted mapping according to the position of video chunk to meet the dynamic segment and rate features of HAS. In order to train and test these methods, we build a real-time HAS video-on-demand (VOD) system in LTE network and do subjective test in different video scenes. With the mean opinion score (MOS), the regression and classification methods are trained to predict the HAS QoE. The validated results show that the proposed ePSNR and WkNN methods outperform other evaluation methods.

创新点

本文提出了针对LTE网络下基于数据挖掘方法的自适应流媒体QoE评估方案。针对自适应流媒体多片段和多码率的特点, 我们采用数据挖掘的策略对动态视频特征进行回归和分类, 通过使用差值信噪比统计量和改进的动态视频特征映射的方法, 对自适应流媒体进行QoE评估。通过搭建的LTE实时点播系统上进行实际视频测试, 结果表明提出的方法较传统线性评估方法准确度更高。

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Authors and Affiliations

  1. School of Information Science and Engineering, Beijing Institute of Technology, Beijing, 100081, China

    Fei Wang, Zesong Fei, Jing Wang, Yifan Liu & Zhikun Wu

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  1. Fei Wang
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  2. Zesong Fei
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  3. Jing Wang
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  4. Yifan Liu
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Correspondence to Zesong Fei.

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Wang, F., Fei, Z., Wang, J. et al. HAS QoE prediction based on dynamic video features with data mining in LTE network. Sci. China Inf. Sci. 60, 042404 (2017). https://doi.org/10.1007/s11432-015-1044-3

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