Skip to main content
Log in

End-to-End Multimedia Quality Estimation with Robust Optimization in Real-World Mobile Computing Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Quality of experience (QoE) parameters describe the end-to-end (E2E) quality as experienced by the mobile users. These are difficult to measure and quantify. On the one hand, system quality of service (SQoS) parameters are metrics that are close related to the network status, and defined from the viewpoint of the service provider rather than the service user. On the other hand, E2E service quality of service (ESQoS) parameters describe the QoS of the services and they are obtained directly from the QoE parameters by mapping them into parameters more relevant to network operators, service providers and mobile users. A useful technique for mobile network planning and optimization is to build quality estimation models for mobile voice and video telephony service. Our research is focused on developing statistical estimation models extracted by measurements acquired via a drive-test measurement campaign of a commercial UMTS multimedia network. Regression estimates, computed with a robust optimization strategy, suggest a weaker dependence between the SQoS and ESQoS parameters and connect the strength of the dependence with the accuracy of the measurements used to compute the estimates.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. De Moor, K., Ketyko, I., Joseph, W., Deryckere, T., De Marez, L., Martens, L., et al. (2010). Proposed framework for evaluating quality of experience in a mobile, testbed-oriented living lab setting. Mobile Networks and Applications, 15, 378–391.

    Article  Google Scholar 

  2. ETSI TR 126 944 v10.0.0. (2011, April). End-to-end multimedia services performace metrics. European Telecommunications Standards Institute.

  3. Soldani, D., Li, M., & Cuny, R. (2006). QoS and QoE management in UMTS cellular systems. New York: Wiley.

    Book  Google Scholar 

  4. Malkowski, M., & Claßen, D. (2008). Performance of video telephony services in UMTS using live measurements and network emulation. Wireless Personal Communications, 46, 19–32.

    Article  Google Scholar 

  5. Kone, V., Zheng, H., Rowstron, A., O’Shea, G., & Zhao, B. Y. (2013). Measurement-based design of roadside content delivery systems. IEEE Transactions on Mobile Computing, 12(6), 1160–1173.

    Article  Google Scholar 

  6. Gorlatova, M., Wallwater, A., & Zussman, G. (2013). Networking low-power energy harvesting devices: Measurements and algorithms. IEEE Transactions on Mobile Computing, 12(9), 1853–1865.

    Article  Google Scholar 

  7. Amzallag, D., Bar-Yehuda, R., Raz, D., & Scalosub, G. (2013). Cell selection in 4g cellular networks. IEEE Transactions on Mobile Computing, 12(7), 1443–1455.

    Article  Google Scholar 

  8. Lin, Y.-B., Huang-Fu, C.-C., & Alrajeh, N. (2013). Predicting human movement based on telecom’s handoff in mobile networks. IEEE Transactions on Mobile Computing, 12(6), 1236–1241.

    Article  Google Scholar 

  9. Louafi, H., Coulombe, S., & Chandra, U. (2013). Quality prediction-based dynamic content adaptation framework applied to collaborative mobile presentations. IEEE Transactions on Mobile Computing, 12(10), 2024–2036.

    Article  Google Scholar 

  10. Fiedler, M., Wac, K., Bults, R., & Arlos, P. (2013). Estimating performance of mobile services from comparative output-input analysis of end-to-end throughput. IEEE Transactions on Mobile Computing, 12(9), 1761–1773.

    Article  Google Scholar 

  11. Lovrenčič, T., Štular, M., Kačič, Z., & Žgank, A. (2015). Qos estimation and prediction of input modality in degraded ip networks. Wireless Personal Communications, 80(2), 837–857.

  12. Sun, L., & Ifeachor, E. C. (2006). Voice quality prediction models and their application in VoIP networks. IEEE Transactions on Multimedia, 8(4), 809–820.

    Article  Google Scholar 

  13. Reichl, P., Tuffin, B., & Schatz, R. (2013). Logarithmic laws in service quality perception: where microeconomics meets psychophysics and quality of experience. Telecommunication Systems, 52(2), 587–600.

    Google Scholar 

  14. Shaikh, J., Fiedler, M., & Collange, D. (2010). Quality of experience from user and network perspectives. Annales des Télécommunications, 65(1–2), 47–57.

    Article  Google Scholar 

  15. Pitas, C. N., Charilas, D. E., Panagopoulos, A. D., Chatzimisios, P., & Constantinou, P. (2012). ANFIS-based quality prediction models for amr telephony in public 2G/3G mobile networks. In IEEE global communications conference (GLOBECOM).

  16. Pitas, C. N., Charilas, D. E., Panagopoulos, A. D., & Constantinou, P. (2013). Adaptive neuro-fuzzy inference models for speech and video quality prediction in real-world mobile communication networks. IEEE Wireless Communications, 20(3), 80–88.

    Article  Google Scholar 

  17. Casas, P., Seufert, M., & Schatz, R. (2013). YOUQMON: A system for on-line monitoring of YouTube QoE in operational 3G networks. SIGMETRICS Performance Evaluation Review, 41(2), 44–46.

    Article  Google Scholar 

  18. Pitas, C. N., Panagopoulos, A. D., & Constantinou, P. (2015). Quality of consumer experience data mining for mobile multimedia communication networks: Learning from measurements campaign. Wireless and Mobile Computing, 8(1), 34–44.

  19. Bertsimas, D., & Sim, M. (2004). The price of robustness. Operations Research, 52(1), 35–53.

    Article  MATH  MathSciNet  Google Scholar 

  20. Fertis, A. G. (2009). A robust optimization approach to statistical estimation problems. PhD Thesis, MIT.

  21. Soyster, A. L. (1973). Convex programming with set-inclusive constraints and applications to inexact linear programming. Operations Research, 21(5), 1154–1157.

    Article  MATH  MathSciNet  Google Scholar 

  22. Ben-Tal, A., El Ghaoui, L., & Nemirovski, A. S. (2009). Robust optimization. Princeton: Princeton University Press.

    Book  MATH  Google Scholar 

  23. Boyd, Stephen, & Vandenberghe, Lieven. (2004). Convex optimization. New York, NY: Cambridge University Press.

    Book  MATH  Google Scholar 

  24. Fertis, A., Baes, M., & Lüthi, H.-J. (2012). Robust risk management. European Journal of Operational Research, 222(3), 663–672.

    Article  MATH  MathSciNet  Google Scholar 

  25. Fertis, A., Baes, M., & Lüthi, H.-J. (2011). The regularization aspect of optimal-robust conditional value-at-risk portfolios. In Proceedings of the international conference on operations research (OR’11).

  26. Pitas, C. N., Fertis, A. G., Panagopoulos, A. D., & Constantinou, P. (2011). Robust optimization in non-linear regression for speech and video quality prediction in mobile multimedia networks. In Proceedings of the international conference on operations research (OR’11).

  27. SwissQual AG Diversity Benchmarker.

  28. ETSI TS 102 250 (All Parts). (2011). QoS aspects for popular services in GSM and 3G networks. European Telecommunications Standards Institute.

  29. MATLAB. Statistics toolbox, linear and nonlinear modeling. The MathWorks.

  30. Polik, I. SeDuMi 1.3.

  31. Electron. Commun. Committee (ECC) Report 118. (2008, May). Monitoring methodology to assess the performance of GSM networks. In European conference of postal and telecommunications administrations (CEPT).

  32. Holma, H., & Toskala, A. (2007). WCDMA for UMTS: HSPA evolution and LTE. New York: Wiley.

    Book  Google Scholar 

  33. Electron. Commun. Committee (ECC) Report 103. (2007, May). UMTS coverage measurements. In European conference of postal and telecommunications administrations (CEPT).

  34. ETSI TS 136 214 Ver. 11.1.0 (2012-12). LTE; evolved universal terrestrial radio access (E-UTRA); physical layer; measurements (3GPP TS 36.214). European Telecommunications Standards Institute, 2012.

  35. ETSI TR 102 493 Ver. 1.2.1 (2009-06). Guidelines for the use of video quality algorithms for mobile applications. European Telecommunications Standards Institute, 2009.

  36. ITU-T P.862. (2001, February).Perceptual evaluation of speech quality (PESQ): An objective method for end-to-end speech quality assessment of narrow-band telephone networks and speech codecs. International Telecommunication Union.

  37. Eden, A. (2007). No-reference estimation of the coding PSNR for H.264-coded sequences. IEEE Transactions on Consumer Electronics, 53(2), 667–674.

    Article  Google Scholar 

  38. Huynh-Thu, Q., & Ghanbari, M. (2008). Scope of validity of PSNR in image/video quality assessment. Electronics Letters, 44(13), 800–801.

    Article  Google Scholar 

  39. 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 

  40. Hayashi, T., & Yamagishi, K. (2005). State of the art of multimedia quality assessment methods. In ITU-T workshop video and image coding and application (VICA).

  41. ITU-T P. 911. (1998, December). Subjective audiovisual quality assessment methods for multimedia applications. International Telecommunication Union.

  42. Xu, H., Caramanis, C., & Mannor, S. (2010). Robust regression and lasso. IEEE Transactions on Information Theory, 56(7), 3561–3574.

    Article  MathSciNet  Google Scholar 

  43. Sturm, J. F. (1999). Using SeDuMi 1.02, a MATLAB toolbox for optimization over symmetric cones. Optimization Methods and Software: Special Issue on Interior Point Methods, 11(1–4), 625–653.

    Article  MathSciNet  Google Scholar 

  44. Polik, I., Terlaky, T., & Zinchenko, Y. (2007). SeDuMi: A package for conic optimization. In IMA workshop optimization and control.

Download references

Acknowledgments

Experimental equipment was acquired by Mobile Radiocommunications Laboratory, NTUA, during \(AKM\Omega N\) project funded by the General Secretariat of Research and Technology, Ministry of Development, Greece. We would like to thank Mr. A. Tollenaar from SwissQual AG for making available the data of video telephony.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Charalampos N. Pitas.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pitas, C.N., Fertis, A.G. & Panagopoulos, A.D. End-to-End Multimedia Quality Estimation with Robust Optimization in Real-World Mobile Computing Networks. Wireless Pers Commun 84, 2363–2383 (2015). https://doi.org/10.1007/s11277-015-2709-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-015-2709-3

Keywords

Navigation