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SSIM-based adaptation for DASH with SVC in mobile networks

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Abstract

Dynamic Adaptive Streaming over HTTP (DASH) depends on adjustment of the quality of a video stream to the available network conditions. In order to increase Quality of Experience, average video quality should be maximized, while keeping the quality switching frequency at low levels. However, achieving high average quality with low switching frequency in highly fluctuating mobile network conditions is a tricky optimization problem. In order to overcome this problem, dynamic structure of Scalable Video Coding (SVC) is utilized in this paper. Another challenge in the quality adaptation algorithms is to proper assessment of the video quality. Most of the adaptation algorithms takes either bitrate or representation level as the input that is used to evaluate the quality of the video. However, bitrate is not strongly correlated with the quality, as it depends on the content of the video. Likewise, representation quality relationship entirely bound to encoding. In this paper, in order to have a more reliable adaptation input, SSIM is used while representing the quality of the video stream. The proposed adaptation is compared with a successful SVC DASH adaptation algorithm using both subjective and objective tests. As a result, considerably higher scores are achieved in terms of both switching frequency and average quality.

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References

  1. Cisco, Cisco visual networking index: forecast and methodology 2016–2021 (2016)

  2. Stockhammer, T.: Dynamic adaptive streaming over HTTP: standards and design principles. In: Proceedings of the Second Annual ACM Conference On Multimedia Systems, pp. 133–144 (2016)

  3. Schwarz, H., Marpe, D., Wiegand, T.: Overview of the scalable video coding extension of the H. 264/AVC standard. IEEE Transactions on circuits and systems for video technology, 17(9), pp. 1103-1120 (2007)

  4. Kalva, H., Adzic, V., Furht, B.: Comparing MPEG AVC and SVC for adaptive HTTP streaming. In: 2012 IEEE International Conference on Consumer Electronics (ICCE), pp. 158-159. IEEE (2012)

  5. Zhao, M., Gong, X., Liang, J., Wang, W., Que, X., Cheng, S.: QoE-driven cross-layer optimization for wireless dynamic adaptive streaming of scalable videos over HTTP. IEEE Trans. Circuits Syst. Video Technol. 25(3), 451–465 (2015)

    Article  Google Scholar 

  6. Kua, J., Armitage, G., Branch, P.: A survey of rate adaptation techniques for dynamic adaptive streaming over HTTP. IEEE Commun. Surv. Tutor. 19(3), 1842–1866 (2017)

    Article  Google Scholar 

  7. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  8. Xiong, P., Shen, J., Wang, Q., Jayasinghe, D., Li, J., Pu, C.: NBS: A network-bandwidth-aware streaming version switcher for mobile streaming applications under fuzzy logic control. In: 2012 IEEE First International Conference on Mobile Services, pp. 48-55. IEEE (2012)

  9. Chiariotti, F., Pielli, C., Zanella, A., Zorzi, M.: QoE-aware Video Rate Adaptation algorithms in multi-user IEEE 802.11 wireless networks. In: 2015 IEEE International Conference on Communications (ICC), pp. 6116-6121 (2015)

  10. Mori, S., Bandai, M.: QoE-aware quality selection method for adaptive video streaming with scalable video coding. In: 2018 IEEE International Conference on Consumer Electronics (ICCE), pp. 1-4. IEEE (2018)

  11. Sieber, C., Hoßfeld, T., Zinner, T., Tran-Gia, P., Timmerer, C.: Implementation and User-centric comparison of a novel adaptation logic for DASH with SVC. In: 2013 IFIP/IEEE International Symposium on Integerated Network Management (IM 2013), pp. 1318-1323. IEEE (2013)

  12. Miller, K., Quacchio, E., Gennari, G., Wolisz, A.: Adaptation algorithm for adaptive streaming over HTTP. In: 2012 19th International Packet Video Workshop (PV), pp. 173–178. IEEE (2012)

  13. Muller, C., Lederer, S., Timmerer, C.: An evaluation of dynamic adaptive streaming over HTTP in vehicular environments. In: Proceedings of the 4th Workshop on Mobile Video, pp. 37-42. ACM (2012)

  14. Oechsner, S., Zinner, T., Prokopetz, J.,Hoßfeld, T.: Supporting scalable video codecs in a P2Pvideo-on-demand streaming system. In: Proceedings of 21st ITC SS Multimedia Application–Traffic, Perform. QoE, pp. 48–53 (2010)

  15. Hu, S., Sun, L., Gui, C., Jammeh, E., Mkwawa, I.-H.: Content-aware adaptation scheme for QoE optimized DASH applications. In: Global Communications Conference (GLOBECOM), pp. 1336–1341. IEEE (2014)

  16. Zhou, C., Lin, C.-W., Guo, Z.: mDASH: a Markov decision-based rate adaptation approach for dynamic HTTP streaming. IEEE Trans. Multimed. 18(4), 738–751 (2016)

    Article  Google Scholar 

  17. Ozcan, S. G., Kivilcim, T., Cetinkaya, C., Sayit, M.: Rate adaptation algorithm with backward quality increasing property for SVC-DASH. In: 2017 IEEE 7th International Conference on Consumer Electronics-Berlin (ICCE-Berlin), pp. 24-28. IEEE (2017)

  18. Lekharu, A., Kumar, S., Sur, A., Sarkar, A.: A qoe aware svc based client-side video adaptation algorithm for cellular networks. In: Proceedings of the 19th International Conference on Distributed Computing and Networking, pp. 27. ACM (2018)

  19. Reichel, J., Schwarz, H., Wien, M.: Joint scalable video model 11 (JSVM 11). Joint Video Team, pp. 23 (2007)

  20. Stenberg, D.: Libcurl—the multiprotocol file transfer library. https://curl.haxx.se/libcurl/. Accessed 1 April 2019

  21. Kreuzberger, C., Posch, D., Hellwagner, H.: A scalable video coding dataset and toolchain for dynamic adaptive streaming over HTTP. In: Proceedings of the 6th ACM Multimedia Systems Conference, pp. 213-218. ACM (2015)

  22. Blender Foundation: Big Buck Bunny. https://peach.bl-ender.org/. Accessed 1 Feb 2019

  23. Blender Foundation: Sintel, the Durian Open Movie Project. https://durian.blender.org/. Accessed 1 Mar 2019

  24. Blender Foundation: Tears of Steel. https://mango.bl-ender.org/. Accessed 1 Feb 2019

  25. Wang, C., Rizk, A. Zink, M.: SQUAD: A spectrum-based quality adaptation for dynamic adaptive streaming over HTTP. In: Proceedings of the 7th International Conference on Multimedia Systems, pp. 1, ACM (2016)

  26. ITU-T Recommendation P.910: Subjective video quality assessment methods for multimedia applica-tions. International telecommunication union (1999)

  27. Bradley, R.A., Terry, M.E.: Rank analysis of incomplete block designs: I. Method paired comparisons. Biometrika 18(3), 324–345 (1952)

    MathSciNet  MATH  Google Scholar 

  28. Lee, J.-S., De Simone, F., Ebrahimi, T.: Subjective quality evaluation via paired comparison: application to scalable video coding. IEEE Trans. Multimed. 13(5), 882–883 (2011)

    Article  Google Scholar 

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Correspondence to Nükhet Özbek.

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Çalı, M., Özbek, N. SSIM-based adaptation for DASH with SVC in mobile networks. SIViP 14, 1107–1114 (2020). https://doi.org/10.1007/s11760-020-01646-y

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