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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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References

  1. 1

    Cisco Systems Inc. Cisco visual networking index: global mobile data traffic forecast update 2014–2019 white paper. http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/completewhite-paper-c11-481360.html. 2015

  2. 2

    Zhou Y Q, Liu H, Pan Z G, et al. Spectral and energy efficient two-stage cooperative multicast for LTE-advanced and beyond. IEEE Wirel Commun, 2014, 21: 34–41

  3. 3

    Zhang Z S, Long K P, Vasilakos A V, et al. Full-duplex wireless communications: challenges, solutions and future research directions. Proc IEEE, 2016, 99: 1–41

  4. 4

    Zhou Y Q, Liu H, Pan Z G, et al. Two-stage cooperative multicast transmission with optimized power consumption and guaranteed coverage. IEEE J Sel Areas Commun, 2014, 32: 274–284

  5. 5

    Fei Z S, Luo C, Xing C W, et al. Joint linear processing design for distributed two-way amplify-and-forward MIMO relaying networks. China Commun, 2013, 10: 126–133

  6. 6

    Zambelli A. Smooth streaming technical overview. http://www.iis.net/learn/media/on-demand-smooth-streaming/smooth-streaming-technical-overview. 2009

  7. 7

    Apple Inc. HTTP live streaming overview. https://developer.apple.com/streaming. 2014

  8. 8

    Adobe Systems Inc. HTTP dynamic streaming. http://www.adobe.com/products/hds-dynamicstreaming.html. 2015

  9. 9

    3GPP. Transparent end-to-end packet-switched streaming service (PSS); progressive download and dynamic adaptive streaming over HTTP (3GP-DASH). TS 26.247 V13.1.0. http://www.3gpp.org/ftp/Specs/archive/26-series/26.247/26247-d10.zip. 2015

  10. 10

    Video Quality Experts Group. VQEG FRTV phase I final report. http://www.its.bldrdoc.gov/vqeg/-projects/frtvphasei/frtv-phase-i.aspx. 2000

  11. 11

    Xue J T, Zhang D Q, Yu H, et al. Assessing quality of experience for adaptive HTTP video streaming. In: Proceedings of the IEEE International Conference on Multimedia and Expo Workshops, Chengdu, 2014. 1–6

  12. 12

    Oyman O, Singh S. Quality of experience for HTTP adaptive streaming services. IEEE Commun Mag, 2012, 50: 20–27

  13. 13

    Liu Y T, Shen Y, Mao Y N, et al. A study on quality of experience for adaptive streaming service. In: Proceedings of the IEEE International Conference on Communications Workshops, Budapest, 2013. 682–686

  14. 14

    Alberti C, Renzi D, Timmerer C, et al. Automated QoE evaluation of dynamic adaptive streaming over HTTP. In: Proceedings of the 5th International Workshop on Quality of Multimeda Experiecne, Klagenfurt, 2013. 58–63

  15. 15

    de Vriendt J, de Vleeschauwer D, Robinson D. Model for estimating QoE of video delivered using HTTP adaptive streaming. In: Proceedings of the IFIP/IEEE International Symposium on Inergrated Network Management, Ghent, 2013. 1288–1293

  16. 16

    NGMN Alliance. Service quality definition and measurement. White Paper Version 1. http://www.ngmn.org/publications/all-downloads/article/technical-report-service-quality-definition-andmeasurement.html. 2013

  17. 17

    Fei Z S, Xing C W, Li N. QoE-driven resource allocation for mobile IP services in wireless network. Sci China Inf Sci, 2015, 58: 012301

  18. 18

    Zhang Z S, Chai X M, Long K P, et al. Full-duplex techniques for 5G networks: self-interference cancellation, protocol design and relay selection. IEEE Commun Mag, 2015, 53: 128–137

  19. 19

    Germak G, Pinson M, Wolf S. The relationship among video quality, screen resolution, and bit rate. IEEE Trans Broadcast, 2011, 57: 258–262

  20. 20

    Belmudez B, Moller S. An approach for modeling the effects of video resolution and size on the perceived visual quality. In: Proceedings of the IEEE International Symposium, Dana Point, 2011. 464–469

  21. 21

    Xing C W, Ma Y, Zhou Y Q, et al. Transceiver optimization for multi-hop communications with per-antenna power constraints. IEEE Trans Signal Process, 2016, 64: 1519–1534

  22. 22

    Fei Z S, Li N, Xing C W, et al. Energy-efficient transceiver design for multi-pair two-way relay systems. China Commun, 2015, 12: 133–140

  23. 23

    ITU-T. Mean opinion score terminology. P.800.1. http://www.itu.int/rec/T-REC-P.800.1-201602-I. 2006

  24. 24

    ITU-T. Mean opinion score interpretation and reporting. P.800.2. http://www.itu.int/rec/T-REC-P.800.2-201305-I. 2013

  25. 25

    ITU-T. Subjective video quality assessment methods for multimedia applications. P.910. http://www.itu.int/rec/TREC-P.910-200804-I. 2008

  26. 26

    Fiedler M, Hossfeld T, Tran G P. A generic quantitative relationship between quality of experience and quality of service. IEEE Netw, 2010, 24: 36–41

  27. 27

    Zhao S, Jiang H, Cai Q, et al. Hybrid framework for no-reference video quality indication over LTE networks. In: Proceedings of Wireless and Optical Communication Conference, Newark, 2014. 1–5

  28. 28

    Ghalut T, Larijani H, Shahrabi A. Content-Based video quality prediction using random neural networks for video streaming over LTE networks. In: Proceedings of IEEE International Conference on Computer and Information Technology, Liverpool, 2015. 1626–1631

  29. 29

    Zhang Z S, Long K P, Wang J P, et al. On swarm intelligence inspired self-organized networking: its bionic mechanisms, designing principles and optimization approaches. IEEE Commun Surv Tut, 2014, 16: 513–537

  30. 30

    3GPP. Evolved universal terrestrial radio access (E-UTRA) and evolved universal terrestrial radio access network (E-UTRAN); overall description; stage 2. TS 36.300. http://www.3gpp.org/DynaReport/36300.htm. 2014

  31. 31

    3GPP. Evolved universal terrestrial radio access network (E-UTRAN); architecture description. TS 36.401. http://www.3gpp.org/DynaReport/36401.htm. 2014

  32. 32

    3GPP. Evolved universal terrestrial radio access network (E-UTRAN); S1 general aspects and principles. TS 36.410. http://www.3gpp.org/DynaReport/36410.htm. 2014

  33. 33

    Zhang Z S, Long K P, Wang J P. Self-organization paradigms and optimization approaches for cognitive radio technologies: a survey. IEEE Wirel Commun, 2013, 20: 36–42

  34. 34

    Xing C W, Gao F F, Zhou Y Q. A framework for transceiver designs for multi-hop communications with covariance shaping constraints. IEEE Trans Signal Process, 2015, 63: 3930–3945

  35. 35

    Apache Software Foundation. Apache HTTP Server. Version 2.2.21. http://httpd.apache.org/.2015

  36. 36

    Fei Z S, Ding H C, Xing C W, et al. Performance analysis for range expansion in heterogeneous networks. Sci China Inf Sci, 2014, 57: 082305

  37. 37

    Xing C W, Ma S D, Fei Z S. A general robust linear transceiver design for multi-hop amplify-and-forward MIMO relaying systems. IEEE Trans Signal Process, 2013, 61: 1196–1209

  38. 38

    Blender Foundation. Watch Sintel Online. https://durian.blender.org/download. 2015

  39. 39

    Rimac D S, Vranjes M, Zagar D. Influence of temporal pooling method on the objective video quality evaluation. In: Proceedings of International Symposium on Broadband Multimedia Systems and Broadcasting, Bilbao, 2009. 1–5

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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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Keywords

  • HTTP adaptive streaming (HAS)
  • quality of experience (QoE)
  • regression
  • classification
  • data mining
  • video-on-demand (VOD)
  • long term evolution (LTE)

关键词

  • 自适应流媒体
  • 用户体验质量
  • 回归
  • 分类
  • 数据挖掘
  • 点播
  • LTE