Multimedia traffic quality of service management using statistical and artificial intelligence techniques

Multimedia traffic quality of service management using statistical and artificial intelligence techniques

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Circuits, Devices & Systems — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Managing quality of service (QoS) is an important network operation, especially in hybrid wired and wireless multimedia networks. In this study, a two-stage approach to intelligently manage QoS for multimedia traffic was developed. Voice over Internet protocol (VoIP) was included in the study as an example of a typical multimedia application. Initially an adaptive statistical sampling technique was employed. It determined the traffic's statistics and used them in a fuzzy inference system to determine the optimum interval between every two consecutive sections of the traffic sampled. In the second stage, a fuzzy c-means (FCM) clustering was used to pre-process QoS parameters (delay, jitter and packet loss ratio) obtained from the devised sampling scheme. A multilayer perceptron (MLP) neural network then used the information from FCM to assess the QoS provided for VoIP.

It was shown that the developed adaptive statistical sampling represents the traffic more correctly than the systematic, stratified and random non-adaptive sampling methods. Also, the combination of statistical sampling followed by FCM and MLP accurately indicated the QoS for VoIP.


    1. 1)
      • 1. Mohamed, S., Rubino, G., Cervantes, F., Afifi, H.: ‘Real-time video quality assessment in packet networks: a neural network model’. Proc. Int. Conf. IEEE Transactions on Circuits and Systems for Video Technology, 2001, pp. 121.
    2. 2)
    3. 3)
      • 3. Zseby, T.: ‘Comparison of sampling methods for non-intrusive SLA validation’. Second Workshop on End-to-End Monitoring Techniques and Services, 2004, pp. 18.
    4. 4)
      • 4. Claffy, K.C., Polyzos, G.C., Braun, H.W.: ‘Application of sampling methodologies to network traffic characterization’. Conf. Proc. Communications Architectures, Protocols and Applications, 1993, pp. 194203.
    5. 5)
      • 5. Giertl, J., Jakab, F., Baca, J., Andoga, R., Mirilovic, M.: ‘Contribution to adaptive sampling of QoS parameters in computer networks’, Acta Electrotechn. Inf., 2006, 6, (1), pp. 18.
    6. 6)
      • 6. Ma, W., Huang, C., Yan, J.: ‘Adaptive sampling for network performance measurement under voice traffic’. Proc. Int. Conf. IEEE on Communications, 2004, pp. 11291134.
    7. 7)
      • 7. Gan, Y., Zhang, Y., Qian, D.: ‘Adaptive sampling measurement for high speed network traffic flow’. Proc. Int. Conf. IEEE on Wireless Communications, Networking and Mobile Computing, 2009, pp. 40854088.
    8. 8)
      • 8. Timo, L., Hannu, K., Tapani, H.: ‘Profiling network applications with fuzzy c-means clustering and SOM’. Proc. Int. Conf. on Fuzzy Systems and Knowledge Discovery, 2002, pp. 15.
    9. 9)
      • 9. Chen, B., Hu, J., Duan, L., Gu, Y.: ‘Network administrator assistance system based on fuzzy c-means analysis’, J. Adv. Comput. Intell. Intell Inform., 2009, 13, (2), pp. 9196.
    10. 10)
      • 10. Wang, T., Liang, Z., Zhao, C.: ‘A detection method for routing attacks of wireless sensor network based on fuzzy c-means clustering’. Proc. Int. Conf. IEEE on Fuzzy Systems and Knowledge Discovery, 2009, pp. 445449.
    11. 11)
      • 11. Ting, B., Yong, W., Xiaoling, T.: ‘Network traffic classification based on kernel self-organizing maps’. Proc. Int. Conf. on Intelligent Computing and Integrated Systems, 2010, pp. 310314.
    12. 12)
      • 12. Palomar, D., Skehill, R., Rice, I., Picovici, D., McGrath, S.: ‘Objective assessment of audio quality’. Proc. Int. Conf. IET on Signals and Systems, 2008, pp. 3742.
    13. 13)
      • 13. Mishra, R., Sharma, V.: ‘QoS routing in MPLS networks using active measurements’. Proc. Int. Conf. IEEE on Convergent Technologies for Asia-Pacific Region, 2003, pp. 323327.
    14. 14)
      • 14. Cranley, N., Davis, M.: ‘Performance evaluation of video streaming with background traffic over IEEE 802.11 WLAN networks’. Proc. Int. ACM Workshop on Wireless Multimedia Networking and Performance Modelling, 2005, pp. 131139.
    15. 15)
      • 15. Nogueira, A., Salvador, P., Valadas, R.: ‘Predicting the quality of service of wireless LANs using neural networks’. Proc. Int. ACM Symp. on Modelling, Analysis, and Simulation of Wireless and Mobile Systems ACM, New York, NY, USA, 2006, pp. 5260.
    16. 16)
    17. 17)
      • 17. Dogman, A., Saatchi, R., Al-Khayatt, S.: ‘Evaluation of computer network quality of service using neural network’. Proc. Int. IEEE Symp. on Business, Engineering and Industrial Applications, 2012, pp. 217222.
    18. 18)
    19. 19)
      • 19. ITU Recommendation, G.1010: ‘End-user multimedia QoS categories’. Telecommunication Standardization Sector of ITU, 2001, pp. 118.
    20. 20)
      • 20. Wang, P.Y., Yemini, Y., Florissi, D., Zinky, J., Florissi, P.: ‘Experimental QoS performances of multimedia applications’. Proc. Int. Conf. IEEE Computer and Communications Societies, 2000, pp. 970979.
    21. 21)
      • 21. Saraireh, M., Saatchi, R., Al-Khayatt, S., Strachan, R.: ‘Assessment and improvement of quality of service using fuzzy logic and hybrid genetic-fuzzy approaches’, ACM, 2007, 27, (2), pp. 95111.
    22. 22)
      • 22. Al-Sbou, Y., Saatchi, R., Al-khayatt, S., Strachan, R.: ‘Quality of service assessment of multimedia traffic over wireless ad hoc networks’. Proc. Int. Conf. Communication Systems, Networks and Digital Signal Processing, 2006, pp. 129133.
    23. 23)
      • 23. Xie, X., Beni, G.: ‘A validity measure for fuzzy clustering’. Proc. Int. Conf. IEEE Pattern Analysis and Machine Intelligence, 1991, pp. 841847.
    24. 24)
      • 24. Abraham, A.: ‘Artificial neural network’, in Sydenham, P., , Thorn, R. (eds.): ‘Handbook of measuring system design’ (John Wiley, 2005), pp. 901908.
    25. 25)
      • 25. Karlik, B., Olgac, A.: ‘Performance analysis of various activation functions in generalized MLP architectures of neural networks’, Int. J. Artif. Intell. Expert Syst., 2010, 1, (4), pp. 111122.
    26. 26)
      • 26. Eberhart, R.C., Dobbins, R.W.: ‘Neural network PC tools: a practical guide’ (Academic Press, 1990).
    27. 27)
      • 27. ‘Network Simulator 2’., accessed March 2012.
    28. 28)
      • 28. ‘YUV QCIF Reference Videos’,, accessed January 2012.
    29. 29)
      • 29. ‘IEEE Computer Society LAN/MAN Standards Committee’, Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications Amendment 8: Medium Access Control (MAC) Quality of Service Enhancements, 2005.

Related content

This is a required field
Please enter a valid email address