Advertisement

QoE Management Framework

  • Vlado MenkovskiEmail author
Chapter
First Online:
  • 284 Downloads
Part of the Springer Theses book series (Springer Theses)

Abstract

The focus of this chapter is on monitoring and management of delivered QoE in multimedia systems. Monitoring the QoE typically involves collecting a wide range of available quality performance indicators (QPI) and successfully interpreting these measurements. In this manner, QoE frameworks typically consist of a set of sensors or probes that collect performance data (or QPI) from the system that is fed through QoE models to compute the quality. The QoE estimations delivered by the frameworks indicate the level of performance by the system, against which the service provider can execute the management strategies. This chapter starts with a discussion of existing QoE frameworks and continues to introduce a QoE management framework for an IPTV service developed part of this work (Menkovski et al., Second International Conferences on Advances in Multimedia (MMEDIA), 2010) [1] as a demonstration of an end-to-end QoE managment approach.

Keywords

Support Vector Machine Packet Loss Online Learning Concept Drift Decision Tree Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.

References

  1. 1.
    V. Menkovski, G. Exarchakos, A. Liotta, A. Sánchez, Measuring quality of experience on a commercial mobile tv platform, in Second International Conferences on Advances in Multimedia (MMEDIA) (IEEE, 2010), pp. 33–38Google Scholar
  2. 2.
    V. Menkovski, G. Exarchakos, A. Liotta, A. Cuadra Sánchez, Quality of experience models for multimedia streaming. Int. J. Mob. Comput. Multimedia Commun. 2(4), 1–20 (2010). www.igi-global.com/ijmcmc/. doi: 10.4018/jmcmc.2010100101. ISSN:1937-9412
  3. 3.
    V. Menkovski, G. Exarchakos, A. Liotta, The value of relative quality in video delivery, J. Mob. Multimedia 7(3), 151–162 (Rinton Press, 2011). ISSN:1550-4646, http://www.rintonpress.com/xjmm7/jmm-7-3/151-162.pdf
  4. 4.
    F. Agboma, A. Liotta, Quality of experience management in mobile content delivery systems. J. Telecommun. Syst. (special issue on the Quality of Experience issues in Multimedia Provision) 49(1), 85–98 (Springer, 2012). doi: 10.1007/s11235-010-9355-6
  5. 5.
    V. Menkovski, A. Liotta, Adaptive psychometric scaling for video quality assessment. J. Sign. Process.: Image Commun. 26(8), 788–799 (Elsevier, 2012). http://dx.doi.org/10.1016/j.image.2012.01.004
  6. 6.
    G. Exarchakos, L. Druda, V. Menkovski, P. Bellavista, A. Liotta, Skype resilience to high motion videos. Int. J. Wavelets, Multiresolut. Inf. Process. 11(3), (World Scientific Publishing, 2013). http://dx.doi.org/10.1142/S021969131350029X
  7. 7.
    A. Liotta, The cognitive net is coming. IEEE Spectrum 50(8), 26–31 (IEEE, 2013). http://dx.doi.org/10.1109/MSPEC.2013.6565557
  8. 8.
    G. Exarchakos, V. Menkovski, L. Druda, A. Liotta, Network analysis on Skype end-to-end video quality. Int. J. Pervasive Comput. Commun. 11(1) (Emerald, 2015). http://www.emeraldinsight.com/doi/abs/10.1108/IJPCC-08-2014-0044
  9. 9.
    C. Ragusa, A. Liotta, G. Pavlou, An adaptive clustering approach for the management of dynamic systems, IEEE J. Sel. Areas Commun. (JSAC). (special issue on Autonomic Communication Systems) 23(12), 2223–2235 (IEEE, 2005). (IF 4.138; SJR 3.34) http://dx.doi.org/10.1109/JSAC.2005.857203
  10. 10.
    M. Alhaisoni, A. Liotta, Characterization of signalling and traffic in joost. J. P2P Networking Appl. (special issue on Modelling and Applications of Computational P2P) 2, 75–83 (Springer, 2009). doi: 10.1007/s12083-008-0015-5, ISSN:1936-6450
  11. 11.
    M. Alhaisoni, A. Liotta, M. Ghanbari, Resource-awareness and trade-off optimization in P2P video streaming. Int. J. Adv. Media Commun. (special issue on High-Quality Multimedia Streaming in P2P Environments) 4(1), pp. 59–77 (Inderscience Publishers, 2010). doi: 10.1504/IJAMC.2010.030005, ISSN:1741-8003
  12. 12.
    M. Alhaisoni, M. Ghanbari, A. Liotta, Localized multistreams for P2P streaming. Int. J. Digit. Multimedia Broadcast. 843574, 12 pp. (Hindawi, 2010). doi: 10.1155/2010/843574
  13. 13.
    M. Alhaisoni, A. Liotta, M. Ghanbari, Scalable P2P video streaming. Int. J. Bus. Data Commun. Networking 6(3), 49–65 (IGI Global, 2010). doi: 10.4018/jbdcn.2010070103, ISSN:1548-0631
  14. 14.
    A. Liotta, Farewell to deterministic networks, in Proceedings of the 19th IEEE Symposium on Communications and Vehicular Technology in the Benelux, Eindhoven, The Netherlands, 16 Nov 2012 (IEEE). http://dx.doi.org/10.1109/SCVT.2012.6399413
  15. 15.
    M. Torres Vega, E. Giordano, D.C. Mocanu, D. Tjondronegoro, A. Liotta, Cognitive no-reference video quality assessment for mobile streaming services, in Proceedings of the 7th International Workshop on Quality of Multimedia Experience, Messinia, Greece, 26–29 May 2015 (IEEE). http://www.qomex.org
  16. 16.
    M. Torres Vega, D. Constantin Mocanu, R. Barresi, G. Fortino, A. Liotta, Cognitive streaming on android devices, in Proceedings of the 1st IEEE/IFIP IM 2015 International Workshop on Cognitive Network and Service Management, Ottawa, Canada, 11–15 May, 2015 (IEEE). http://www.cogman.org
  17. 17.
    D. Constantin Mocanu, G. Exarchakos, H.B. Ammar, A. Liotta, Reduced reference image quality assessment via boltzmann machines, in Proceedings of the 3rd IEEE/IFIP IM 2015 International Workshop on Quality of Experience Centric Management, Ottawa, Canada, 11–15 May 2015 (IEEE)Google Scholar
  18. 18.
    D. Constantin Mocanu, G. Santandrea, W. Cerroni, F. Callegati, A. Liotta, Network performance assessment with quality of experience benchmarks, in Proceedings of the 10th International Conference on Network and Service Management, Rio de Janeiro, Brazil, 17–21 Nov 2014 (IEEE)Google Scholar
  19. 19.
    M. Torres Vega, S. Zou, D. Constantin Mocanu, E. Tangdiongga, A.M.J. Koonen, A. Liotta, End-to-End performance evaluation in high-speed wireless networks, in Proceedings of the 10th International Conference on Network and Service Management, Rio de Janeiro, Brazil, 17–21 Nov 2014 (IEEE)Google Scholar
  20. 20.
    D.C. Mocanu, A. Liotta, A. Ricci, M. Torres Vega, G. Exarchakos, When does lower bitrate give higher quality in modern video services?, in Proceedings of the 2nd IEEE/IFIP International Workshop on Quality of Experience Centric Management, Krakow, Poland, 9 May 2014 (IEEE). http://dx.doi.org/10.1109/NOMS.2014.6838400
  21. 21.
    A. Liotta, D. Constantin Mocanu, V. Menkovski, L. Cagnetta, G. Exarchakos, Instantaneous video quality assessment for lightweight devices, in Proceedings of the 11th International Conference on Advances in Mobile Computing and Multimedia, Vienna, Austria, 2–4 Dec 2013 (ACM). http://dx.doi.org/10.1145/2536853.2536903
  22. 22.
    V. Menkovski, A. Liotta, Intelligent control for adaptive video streaming, in Proceedings of the International Conference on Consumer Electronics, Las Vegas, US, Jan 11–14, 2013 (IEEE) http://dx.doi.org/10.1109/ICCE.2013.6486825
  23. 23.
    A. Liotta, L. Druda, G. Exarchakos, V. Menkovski, Quality of experience management for video streams: the case of skype, in Proceedings of the 10th International Conference on Advances in Mobile Computing and Multimedia, Bali, Indonesia, 3–5 Dec 2012 (ACM) http://dx.doi.org/10.1145/2428955.2428977
  24. 24.
    V. Menkovski, G. Exarchakos, A. Liotta, Machine learning approach for quality of experience aware networks, in Proceedings of Computational Intelligence in Networks and Systems, Thessaloniki, Greece, 24–26 Nov 2010 (IEEE)Google Scholar
  25. 25.
    V. Menkovski, G. Exarchakos, A. Liotta, Adaptive testing for video quality assessment, in Proceedings of Quality of Experience for Multimedia Content Sharing. Lisbon, Portugal, 29 June 2011 (ACM)Google Scholar
  26. 26.
    G. Exarchakos, V. Menkovski, A. Liotta, Can Skype be used beyond video calling? in Proceedings of the 9th International Conference on Advances in Mobile Computing and Multimedia, Ho Chi Minh City, Vietnam, 5–7 Dec 2011 (ACM)Google Scholar
  27. 27.
    V. Menkovski, G. Exarchakos, A. Liotta, Tackling the sheer scale of subjective QoE, in Proceedings of 7th International ICST Mobile Multimedia Communications Conference, Cagliari, Italy, 5–7 Sept 2011 (Springer, Lecture Notes of ICST, 2012), vol. 29, pp. 1–15. http://www.springerlink.com/content/p1443m265r25756x/. doi: 10.1007/978-3-642-30419-4_1
  28. 28.
    J. Okyere-Benya, M. Aldiabat, V. Menkovski, G. Exarchakos, A. Liotta, Video quality degradation on IPTV networks, in Proceedings of International Conference on Computing, Networking and Communications, Maui, Hawaii, USA, Jan 30–Feb 2, 2012 (IEEE)Google Scholar
  29. 29.
    V. Menkovski, G. Exarchakos, A. Liotta, Online QoE prediction, in Proceedings of the 2nd IEEE International Workshop on Quality of Multimedia Experience, Trondheim, Norway, June 21–23, 2010 (IEEE)Google Scholar
  30. 30.
    V. Menkovski, G. Exarchakos, A. Liotta, Online learning for quality of experience management, in Proceedings of The Annual Machine Learning Conference of Belgium and The Netherlands, Leuven, Belgium, May 27th–28th, 2010. http://dtai.cs.kuleuven.be/events/Benelearn2010/submissions/benelearn2010_submission_20.pdf
  31. 31.
    V. Menkovski, A. Oredope, A. Liotta, A. Cuadra-Sanchez, Optimized online learning for QoE prediction, in Proceedings of the 21st Benelux Conference on Artificial Intelligence, Eindhoven, The Netherlands, 29–30 Oct 2009, pp. 169–176. http://wwwis.win.tue.nl/bnaic2009/proc.html, ISSN:1568-7805
  32. 32.
    K. Yaici, A. Liotta, H. Zisimopoulos, T. Sammut, User-centric uality of service management in UMTS, in Proceedings of the 4th Latin American Network Operations and Management Symposium (LANOMS’05), Porto Alegre, Brazil, 29–31 Aug 2005 (Springer)Google Scholar
  33. 33.
    F. Agboma, A. Liotta, User-centric assessment of mobile content delivery, in Proceedings of the 4th International Conference on Advances in Mobile Computing and Multimedia, Yogyakarta, Indonesia, 4–6 Dec 2006Google Scholar
  34. 34.
    F. Agboma, A. Liotta, Managing the user’s quality of experience, in Proceedings of the second IEEE/IFIP International Workshop on Business-driven IT Management (BDIM 2007). Munich, Germany, 21th May 2007 (IEEE)Google Scholar
  35. 35.
    M. Mu, A. Mauthe, F. Garcia, A utility-based qos model for emerging multimedia applications, in The Second International Conference on 2008 Next Generation Mobile Applications, Services and Technologies, NGMAST’08 (IEEE, 2008), pp. 521–528Google Scholar
  36. 36.
    M. Mu, R. Gostner, A. Mauthe, G. Tyson, F. Garcia, Visibility of individual packet loss on h. 264 encoded video stream–a user study on the impact of packet loss on perceived video quality (2009)Google Scholar
  37. 37.
    J. Kim, T. Um, W. Ryu, B. Lee, Iptv systems, standards and architectures: Part ii-heterogeneous networks and terminal-aware qos/qoe-guaranteed mobile iptv service. Commun. Mag. IEEE 46(5), 110–117 (2008)CrossRefGoogle Scholar
  38. 38.
    M. Volk, J. Sterle, U. Sedlar, A. Kos, An approach to modeling and control of qoe in next generation networks [next generation telco it architectures]. Commun. Mag. IEEE 48(8), 126–135 (2010)CrossRefGoogle Scholar
  39. 39.
    S. Esaki, A. Kurokawa, K. Matsumoto, Overview of the next generation network. NTT Tech. Rev. 5(6), 2007Google Scholar
  40. 40.
    J. Zhang, N. Ansari, On assuring end-to-end qoe in next generation networks: challenges and a possible solution. Commun. Mag. IEEE 49(7), 185–191 (2011)CrossRefGoogle Scholar
  41. 41.
    R. Fielding, J. Gettys, J. Mogul, H. Frystyk, L. Masinter, P. Leach, T. Berners-Lee, Hypertext transfer protocol–http/1.1 (1999)Google Scholar
  42. 42.
    W. Stevens, G. Wright, TCP/IP Illustrated: The Protocols, vol. 1 (Addison-Wesley Professional, 1994)Google Scholar
  43. 43.
    T. Stockhammer, P. Fröjdh, I. Sodagar, S. Rhyu, Information technologympeg systems technologiespart 6: Dynamic adaptive streaming over http (dash). ISO/IEC, MPEG Draft International Standard (2011)Google Scholar
  44. 44.
    H. Schulzrinne, Real time streaming protocol (rtsp) (1998)Google Scholar
  45. 45.
    J. Postel, User datagram protocol, Isi (1980)Google Scholar
  46. 46.
    P. Frossard, Fec performance in multimedia streaming. Commun. Lett. IEEE 5(3), 122–124 (2001)CrossRefGoogle Scholar
  47. 47.
    V. Menkovski, G. Exarchakos, A. Liotta, A. Cuadra-Sánchez, Managing quality of experience on a commercial mobile tv platform. Int. J. Adv. Telecommun. 4(1, 2), 72–81 (2011)Google Scholar
  48. 48.
    TM forum—TM forum IPDR program. http://www.tmforum.org/InDepth/10344/home.html
  49. 49.
    A. Cuadra-Sanchez, C. Casas-Caballero, End-to-end quality of service monitoring in convergent iptv platforms, in Third International Conference on Next Generation Mobile Applications, Services and Technologies, NGMAST’09 (IEEE, 2009), pp. 303–308Google Scholar
  50. 50.
    M. Karam, F. Tobagi, Analysis of the delay and jitter of voice traffic over the internet, in Proceedings INFOCOM, Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies, vol. 2 (IEEE, 2001), pp. 824–833Google Scholar
  51. 51.
    E. Alpaydin, Introduction to machine learning (MIT press, Cambridge, 2004)Google Scholar
  52. 52.
    J. Quinlan, Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)Google Scholar
  53. 53.
    J. Quinlan, C4. 5: programs for machine learning, vol. 1 (Morgan Kaufmann, 1993)Google Scholar
  54. 54.
    V. Vapnik, S. Kotz, Estimation of Dependences Based on Empirical Data, vol.41 (Springer, New York, 1982)Google Scholar
  55. 55.
    J. Platt et al., Sequential minimal optimization: a fast algorithm for training support vector machines (1998)Google Scholar
  56. 56.
    R. Schapire, The strength of weak learnability. Mach. Learn. 5(2), 197–227 (1990)Google Scholar
  57. 57.
    L. Breiman, Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)zbMATHGoogle Scholar
  58. 58.
    Y. Freund, R. Schapire et al., Experiments with a new boosting algorithm, in Machine Learning-International Workshop then Conference (Morgan Kaufmann Publishers, inc., 1996), pp. 148–156Google Scholar
  59. 59.
    F. Agboma, A. Liotta, QoE-aware QoS management, in Proceedings of the 6th International Conference on Advances in Mobile Computing and Multimedia, ser. MoMM ’08 (ACM, New York, NY, USA, 2008), p. 111116. http://doi.acm.org/10.1145/1497185.1497210
  60. 60.
    G.T. Fechner, Elements of Psychophysics (Holt, Rinehart and Winston, 1966)Google Scholar
  61. 61.
    I. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann, 2005)Google Scholar
  62. 62.
    R. Kohavi et al., A study of cross-validation and bootstrap for accuracy estimation and model selection, in International Joint Conference on Artificial Intelligence, vol. 14 (Lawrence Erlbaum Associates Ltd, 1995), pp. 1137–1145Google Scholar
  63. 63.
    P. Domingos, G. Hulten, Mining high-speed data streams, in Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, 2000), pp. 71–80Google Scholar
  64. 64.
    R. Kohavi, C. Kunz, Option decision trees with majority votes, in Machine Learning-International Workshop then Conference (Citeseer, 1997), pp. 161–169Google Scholar
  65. 65.
    B. Pfahringer, G. Holmes, R. Kirkby, New options for hoeffding trees, AI 2007: Advances in Artificial Intelligence (2007), pp. 90–99Google Scholar
  66. 66.
    R. Kohavi, Scaling up the accuracy of naive-bayes classifiers: a decision-tree hybrid, in Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, vol. 7 (1996)Google Scholar
  67. 67.
    J. Gama, R. Rocha, P. Medas, Accurate decision trees for mining high-speed data streams, in Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, 2003), pp. 523–528Google Scholar
  68. 68.
    G. Holmes, R. Kirkby, B. Pfahringer, Stress-testing hoeffding trees. Knowl. Disc. Databases: PKDD 2005, 495–502 (2005)Google Scholar
  69. 69.
    N. Oza, Online bagging and boosting, in IEEE International Conference on Systems, Man and Cybernetics, vol. 3 (IEEE, 2005), pp. 2340–2345Google Scholar
  70. 70.
    N. Oza, S. Russell, Experimental comparisons of online and batch versions of bagging and boosting, in Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, 2001), pp. 359–364Google Scholar
  71. 71.
    F. Agboma, A. Liotta, Addressing user expectations in mobile content delivery. Mob. Inf. Syst. 3(3, 4), 153164 (2007). http://dl.acm.org/citation.cfm?id=1376820.1376823
  72. 72.
    A. Bifet, G. Holmes, B. Pfahringer, R. Kirkby, R. Gavaldà, New ensemble methods for evolving data streams (2009)Google Scholar
  73. 73.
    V. Menkovski, G. Exarchakos, A. Liotta, A. Sánchez, Estimations and remedies for quality of experience in multimedia streaming,” in Third International Conference on Advances in Human-Oriented and Personalized Mechanisms, Technologies and Services (CENTRIC), 2010 (IEEE, 2010), pp. 11–15Google Scholar
  74. 74.
    V. Menkovski, A. Oredope, A. Liotta, A. Sánchez, Predicting quality of experience in multimedia streaming, in Proceedings of the 7th International Conference on Advances in Mobile Computing and Multimedia (ACM, 2009), pp. 52–59Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Philips ResearchEindhovenThe Netherlands

Personalised recommendations

Cite chapter

Buy options