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Adaptive QoE-based architecture on cloud mobile media for live streaming

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Abstract

Nowadays, more than 75% of Internet traffic is multimedia traffic, moreover mobile traffic is growing at a rate of 50% each year. All these data together with the evolution of the cloud infrastructures lead us to develop Cloud Mobile Media (CMM) architectures to support the needs demanded by end users. Nevertheless, due to an inherit higher and variable end to end delay mainly as a result of the virtualization process, new challenges appear in particular for live video streaming applications in order to keep a good Quality of Experience (QoE) of the delivered video. Thus, to keep client’s satisfaction within good levels in terms of Mean Opinion Score (MOS), we propose an adaptive QoE-based architecture running on CMM infrastructures for live streaming services. In order to carry out this goal, we propose an estimation of MOS values using an statistical method based on factor analysis. This estimation is based on different measured variables throughout the CMM infrastructure. In addition, we compare the accuracy of the estimated MOS against well-known publicly available video quality algorithms. With these estimations, our proposal is based on two added controllers to the CMM infrastructure: (a) the Software Defined Network controller that acts as a master and (b) the Media Streamer controller. Each one does different actions on the CMM infrastructure in order to maintain and improve the QoE at each end user. Finally, this architecture has been implemented over a fat tree topology in order to show their functionality. The results show that our proposal works properly and it adapts quickly to the network changes in order to deliver a good MOS.

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References

  1. Cisco: Visual Networking Index Global Mobile Data Traffic Forecast, 2017–2020. White Paper. Technical Report. Cisco Systems, Corp. (2017). http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/mobile-white-paper-c11-520862.html

  2. Dey, S.: 2012 International Conference on Computing, Networking and Communications (ICNC), pp. 929–933 (2012). https://doi.org/10.1109/ICCNC.2012.6167561

  3. Hobfeld, T., Schatz, R., Varela, M., Timmerer, C.: IEEE Commun. Mag. 50(4), 28 (2012). https://doi.org/10.1109/MCOM.2012.6178831

    Article  Google Scholar 

  4. ITU-R, BT.500-13 (2013). https://www.itu.int/rec/R-REC-BT.500-13-201201-I/en

  5. Gorsuch, R.L.: Factor Analysis. Lawrence Erlbaum Associates, Hillsdale (1983)

    MATH  Google Scholar 

  6. Chikkerur, S., Sundaram, V., Reisslein, M., Karam, L.: IEEE Trans. Broadcast. 57(2), 165 (2011). https://doi.org/10.1109/TBC.2011.2104671

    Article  Google Scholar 

  7. ITU-R, P.910 (2008). http://www.itu.int/rec/T-REC-P.910-200804-I

  8. ITS: Final Report from the Video Quality Experts Group on the Validation of Objective Models of Video Quality Assessment, Phase I. Technical Report. Video Quality Experts Group (VQEG) (2000). http://www.its.bldrdoc.gov/vqeg/projects/frtv-phase-i/frtv-phase-i.aspx

  9. RYU controller: component-based software defined networking (SDN). Build SDN Agilely. https://osrg.github.io/ryu/. Accessed 15 Jan2018

  10. Al-Fares, M., Loukissas, A., Vahdat, A.: SIGCOMM Comput. Commun. Rev. 38(4), 63 (2008). https://doi.org/10.1145/1402946.1402967

  11. Garca-Pineda, M., Felici-Castell, S., Segura-Garca, J.: 2017 Fourth International Conference on Software Defined Systems (SDS), pp. 100–105 (2017). https://doi.org/10.1109/SDS.2017.7939148

  12. Chunlin, L., Jianhang, T., Youlong, L.: Clust. Comput. (2017). https://doi.org/10.1007/s10586-017-1171-2

  13. Wang, Q., Xu, K., Izard, R., Kribbs, B., Porter, J., Wang, K.C., Prakash, A., Ramanathan, P.: 2014 IEEE 22nd International Conference on Network Protocols (ICNP), pp. 529–532 (2014). https://doi.org/10.1109/ICNP.2014.84

  14. Kim, M., Han, S., Cui, Y., Lee, H., Cho, H., Hwang, S.: Clust. Comput. 17(3), 605 (2014). https://doi.org/10.1007/s10586-014-0381-0

    Article  Google Scholar 

  15. Cheng, B.: 2014 IEEE 7th International Conference on Cloud Computing (CLOUD), pp. 713–720 (2014). https://doi.org/10.1109/CLOUD.2014.100

  16. Gregoire, J.C., Hamel, A.: 2014 IEEE 15th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp. 1–6 (2014). https://doi.org/10.1109/WoWMoM.2014.6919000

  17. Tran, H.A., Mellouk, A., Hoceini, S.: 2011 First International Symposium on Network Cloud Computing and Applications (NCCA), pp. 14–19 (2011). https://doi.org/10.1109/NCCA.2011.10

  18. ITU-T: P.1201 Amendment 2 Series P: terminals and subjective and objective assessment methods (2013). http://www.itu.int/rec/T-REC-P.1201-201312-I!Amd2

  19. Moorthy, A., Seshadrinathan, K., Soundararajan, R., Bovik, A.: IEEE Trans. Circuits Syst. Video Technol. 20(4), 587 (2010). https://doi.org/10.1109/TCSVT.2010.2041829

    Article  Google Scholar 

  20. Floris, A., Atzori, L., Ginesu, G., Giusto, D.: 2012 IEEE Globecom Workshops (GC Wkshps), pp. 1329–1334 (2012). https://doi.org/10.1109/GLOCOMW.2012.6477775

  21. Sedano, I., Brunnström, K., Kihl, M., Aurelius, A.: EURASIP J. Image Video Process. 2014(1), 1 (2014). https://doi.org/10.1186/1687-5281-2014-4

    Article  Google Scholar 

  22. Tamimi, A.K.A., Jain, R., So-In, C.: ICME, pp. 596–601. IEEE (2010). https://doi.org/10.1109/ICME.2010.5583026

  23. Alasti, M., Neekzad, B., Hui, J., Vannithamby, R.: IEEE Commun. Mag. 48(5), 104 (2010). https://doi.org/10.1109/MCOM.2010.5458370

    Article  Google Scholar 

  24. ITU-R: M.2135-1 49(3), 197 (2009)

  25. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: IEEE Trans. Image Process. 13(4), 600 (2004). https://doi.org/10.1109/TIP.2003.819861

    Article  Google Scholar 

  26. Riverbed: Modeler Suite Simulator (2018). http://www.riverbed.com/products/performance-management-control/network-performance-management/network-simulation.html

  27. Ucar, I., Navarro-Ortiz, J., Ameigeiras, P., Lpez-Soler, J.M.: CoRR abs/1301.5793 (2013). https://doi.org/10.1109/BMSB.2012.6264243

  28. Video. Samples (2016). http://www2.tkn.tu-berlin.de/research/evalvid/cif.html

  29. Shafiq, M.Z., Erman, J., Ji, L., Liu, A.X., Pang, J., Wang, J.: SIGMETRICS Perform. Eval. Rev. 42(1), 367 (2014). https://doi.org/10.1145/2637364.2591975

    Article  Google Scholar 

  30. YouTube: Live encoder settings (2018). https://support.google.com/youtube/answer/2853702?hl=en

  31. FFmpeg: Tools (2016). https://www.ffmpeg.org/

  32. Oyman, O., Foerster, J.R., Tcha, Y., Lee, S.C.: IEEE Commun. Mag. 48(8), 68 (2010). https://doi.org/10.1109/MCOM.2010.5534589

    Article  Google Scholar 

  33. Kaiser, H.T.: Educ. Psychol. Meas. 20, 141–151 (1960). https://doi.org/10.1177/001316446002000116

    Article  Google Scholar 

  34. García-Pineda, M., Felici-Castell, S., Segura-Garcia, J.: Netw. Protoc. Algorithms 8(1), 126 (2016). https://doi.org/10.5296/npa.v8i1.8850

    Article  Google Scholar 

  35. Härdle, W., Simar, L.: Applied Multivariate Statistical Analysis, vol. 22007. Springer (2007). https://doi.org/10.1007/978-3-540-72244-1

  36. Hakiri, A., Gokhale, A., Berthou, P., Schmidt, D.C., Gayraud, T.: Comput. Netw. 75, 453 (2014). https://doi.org/10.1016/j.comnet.2014.10.015

    Article  Google Scholar 

  37. Ishimori, A., Farias, F., Cerqueira, E., Abelem, A.: 2013 Second European Workshop on Software Defined Networks (EWSDN), pp. 81–86 (2013). https://doi.org/10.1109/EWSDN.2013.20

  38. Egilmez, H.E., Tekalp, A.M.: IEEE Trans. Multimed. 16(6), 1597 (2014). https://doi.org/10.1109/TMM.2014.2325791

    Article  Google Scholar 

  39. Jeon, M.H., Lee, B.D., Kim, N.G.: 2014 IEEE 3rd Symposium on Network Cloud Computing and Applications (NCCA), pp. 101–104 (2014). https://doi.org/10.1109/NCCA.2014.24

  40. OpenFlow Protocol. https://www.opennetworking.org/sdn-resources/openflow. Accessed 10 Jan 2018

  41. Sodagar, I.: IEEE MultiMed. 18(4), 62 (2011). https://doi.org/10.1109/MMUL.2011.71

    Article  Google Scholar 

  42. Medved, J., Varga, R., Tkacik, A., Gray, K.: Proceeding of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks 2014, pp. 1–6 (2014). https://doi.org/10.1109/WoWMoM.2014.6918985

  43. Berde, P., Gerola, M., Hart, J., Higuchi, Y., Kobayashi, M., Koide, T., Lantz, B., O’Connor, B., Radoslavov, P., Snow, W., Parulkar, G.: Proceedings of the Third Workshop on Hot Topics in Software Defined Networking, HotSDN ’14, pp. 1–6. ACM, New York (2014). https://doi.org/10.1145/2620728.2620744

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Acknowledgements

This work was supported by the Universitat de València under the Projects UV-INV-PRECOMP14-207134, UV-INVAE15-339582, by the Generalitat Valenciana under the Project GV-2016-002 and by the Ministry of Economy under the Project BIA2016-76957-C3-1-R.

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Correspondence to Miguel Garcia-Pineda.

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Felici-Castell, S., Segura-Garcia, J. & Garcia-Pineda, M. Adaptive QoE-based architecture on cloud mobile media for live streaming. Cluster Comput 22, 679–692 (2019). https://doi.org/10.1007/s10586-018-2876-6

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