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Quality of experience prediction model for progressive downloading over mobile broadcast networks

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Abstract

The paper introduces a new quality of experience (QoE) prediction model for progressive downloading over the mobile broadcast networks. The proposed model covers new QoE metrics, a novel QoE aware buffering method, and a novel QoE measurement method that predicts the perceived service quality in real time. Progressive download is a streaming technology using “play while download” approach. As a case study, progressive downloading is used over the multimedia broadcast multicast service (3GPP’s MBMS) as the underlying network. The broadcast networks are unidirectional delivery platforms, and hence exposed to many unwanted conditions such as the packet losses, delays, and bandwidth problems. Quality of service (QoS) is a way of classification that manages how these conditions are controlled and mapped to the service quality. However, QoS parameters could not reflect how the end-user experience is. At this point, the QoE describes the achieved QoS and the end-user satisfaction with the service. Conventional QoE metrics for multimedia streaming assumes that packet losses cause artifacts, such as blurring and color distortions, on media presentation. However, with the progressive downloading the packet losses could be tolerated, e.g., using forward error correction. With the protection against loss errors, the overall network errors are projected onto the behaviors of the buffering model. The buffering characteristics should be described by well defined states and expected behaviors in that expected behaviors, from the user expectation point of view, are better than the random ones. In this article, first a buffering method for progressive downloading is proposed. Then, a real time QoE measurement method is proposed to map the buffering characteristics to the achieved performance of the service. Finally, some subjective study using mean opinion score are provided to prove the accuracy of the proposed model.

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References

  1. Agboma, F., & Liotta, A. (2012). Quality of experience management in mobile content delivery systems. Telecommunication Systems, 49(1), 85–98.

    Article  Google Scholar 

  2. Bechler, M., Ritter, H., & Schiller, J. H. (2001). Quality of service in mobile and wireless networks: The need for proactive and adaptive applications. Telecommunication Systems, 18(1), 227–242.

    Article  Google Scholar 

  3. Cano, M. D., & Cerdan, F. (2012). Subjective QoE analysis of VoIP applications in a wireless campus environment. Telecommunication Systems, 49(1), 5–15.

    Article  Google Scholar 

  4. Ciszkowski, T. I., et al. (2011). Towards quality of experience-based reputation models for future web service provisioning. Telecommunication Systems, 51(4), 283–295. doi:10.1007/s11235-011-9435-2.

    Article  Google Scholar 

  5. Ding, J. W., Deng, D. J., Lo, Y. K., & Park, J. H. (2011). Perceptual quality based error control for scalable on-demand streaming in next-generation wireless networks. Telecommunication Systems, 52(2), 445–459. doi:10.1007/s11235-011-9447-y.

    Google Scholar 

  6. Huynh-Thu, Q., & Ghanbari, M. (2008). Temporal aspect of perceived quality in mobile video broadcasting. IEEE Transactions on Broadcasting, 54(3), 641–651.

    Article  Google Scholar 

  7. ITU-R BT.500. (2000). Methodology for the subjective assessment of the quality of television pictures.

  8. ITU-R BT.700. (2003). Subjective assessment methodology for video quality.

  9. ITU-T P.800. (2003). Mean opinion score (MOS) terminology.

  10. ITU-T P.910. (2008). Subjective video quality assessment methods for multimedia applications.

  11. ITU-T SG 9, Q 12/9. (2011). Hybrid perceptual/bitstream models.

  12. ITU-T SG12 P.NBAMS. Bitstream model for the assessment of performance of multimedia streaming.

  13. Jammeh, E., et al. (2012). Quality of experience (QoE) driven adaptation scheme for voice/video over IP. Telecommunication Systems, 49(1), 99–111.

    Article  Google Scholar 

  14. Joo, H. J., Hong, B. W., Lee, E. S., & Choi, H. K. (2012). Analysis of IPTV service quality applying real-time QoE measurement technology. Lecture Notes in Electrical Engineering, 179, 103–109.

    Article  Google Scholar 

  15. Joo, H. J., Kim, S. S., Hong, B. W., & Lee, E. S. (2012). Quality measure method for IPTV subscriber unit. Lecture Notes in Electrical Engineering, 181, 585–589.

    Article  Google Scholar 

  16. Kim, H. J., & Choi, S. G. (2010). A study on a QoS/QoE correlation model for QoE evaluation on IPTV service. In Proceedings of 12th International Conference on Advanced Communication Technology (ICACT) (pp. 1377–1382).

  17. Lambrinos, L., & Djouvas, C. (2011). Improving quality of experience in wireless VoIP through novel call scheduling. Telecommunication Systems, 52(4), 1905–1916. doi:10.1007/s11235-011-9473-9.

    Article  Google Scholar 

  18. Latre, S. et al. (2008). On-line estimation of the QoE of progressive download services in multimedia access networks. In Proceedings of International Conference on Internet Computing (pp. 181–187).

  19. Li-yuan, L. et al. (2006). The research of quality of experience evaluation method in pervasive computing environment. In Proceedings of 1st International System on Pervasive Computing and Application (pp. 178–182).

  20. Masugi, M. (2002). QoS mapping of VoIP communication using self-organizing neural network. In Proceedings of IEEE Workshop on IP Operations and Management (pp. 13–17).

  21. Mohamed, S., & Rubino, S. (2002). A study of real-time packet video quality using random neural networks. IEEE Transactions on Circuits Systems Video Technology, 12(12), 1071–1083.

    Article  Google Scholar 

  22. Mok, R., Chan, E., & Chang, R. (2011). Measuring the quality of experience of http video streaming. In Proceedings of IEEE/IFIP IM (pp. 485–492).

  23. Naccari, M., Tagliasacchi, M., & Tubaro, S. (2009). No-reference video quality monitoring for H.264/AVC coded video. IEEE Transactions on Multimedia, 11(5), 932–946.

    Article  Google Scholar 

  24. Paila, T. et al. (2004). FLUTE, file delivery over unidirectional transport, IETF RFC 3926.

  25. Pinson, M., & Wolf, S. (2004). A new standardized method for objectively measuring video quality. IEEE Transactions on Broadcasting, 50(3), 312–322.

    Article  Google Scholar 

  26. Video Quality Experts Group (VQEG). Retrieved June 19, 2012 from http://www.its.bldrdoc.gov/vqeg.

  27. VLC Player Source Codes. Retrieved June 19, 2012 from http://www.videolan.org/vlc/download-sources.html.

  28. Wang, Y. (2006). Survey of objective video quality measurements. EMC Corporation, Hopkinton, MA, Technical Report WPI-CS-TR-06-02.

  29. Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600–612.

    Article  Google Scholar 

  30. Watanabe, K., Okamoto, J., & Kurita, T. (2007). Objective video quality assessment method for evaluating effects of freeze distortion in arbitrary video scenes. Imaging. In Proceedings of Spie-Is & T Electronic (p. 6494).

  31. Winkler, S., & Mohandas, P. (2008). The evolution of video quality measurement: From psnr to hybrid metrics. IEEE Transactions on Broadcasting, 54(3), 660–668.

    Article  Google Scholar 

  32. Yetgin, Z., & Çelik, T. (2012). Efficient progressive downloading over multimedia broadcast multicast service. Computer Networks, 56(2), 533–542.

  33. Yetgin, Z., & Seckin, G. (2007). Progressive download for 3G wireless multicasting In IEEE Proceedings on the Future Generation Communication and Networking CS press (pp. 289–295).

  34. Yetgin, Z., & Seckin, G. (2009). Progressive download for multimedia broadcast multicast service. IEEE MultiMedia, 16(2), 76–85.

    Article  Google Scholar 

  35. 3GPP TSG, Technical Report S4-AHP120. (2004). Mapping of SDUs to radio blocks for FEC simulations.

  36. 3GPP TS 26.346. (2011). Multimedia broadcast/multicast service (MBMS); protocols and codecs, version 9.4.1.

  37. 3GPP TS 26.946. (2011). Multimedia broadcast/multicast service (MBMS); user service guidelines.

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Correspondence to Zeki Yetgin.

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Yetgin, Z., Göçer, Z. Quality of experience prediction model for progressive downloading over mobile broadcast networks. Telecommun Syst 58, 55–66 (2015). https://doi.org/10.1007/s11235-014-9873-8

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