Measuring Interaction QoE in Internet Videoconferencing

  • Prasad Calyam
  • Mark Haffner
  • Eylem Ekici
  • Chang-Gun Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4787)

Abstract

Internet videoconferencing has emerged as a viable medium for communication and entertainment. However, its widespread use is being challenged. This is because videoconference end-users frequently experience perceptual quality impairments such as video frame freezing and voice dropouts due to changes in network conditions on the Internet. These impairments cause extra end-user interaction effort and correspondingly lead to unwanted network bandwidth consumption that affects user Quality of Experience (QoE) and Internet congestion. Hence, it is important to measure and subsequently minimize the extra end-user interaction effort in a videoconferencing system. In this paper, we describe a novel active measurement scheme that considers end-user interaction effort and the corresponding network bandwidth consumption to provide videoconferencing interaction QoE measurements. The scheme involves a “Multi-Activity Packet-Trains” (MAPTs) methodology to dynamically emulate a videoconference session’s participant interaction patterns and corresponding video activity levels that are affected by transient changes in network conditions. Also, we describe the implementation and validation of the Vperf tool we have developed to measure the videoconferencing interaction QoE on a network path using our proposed scheme.

References

  1. 1.
    Tirumala, A., Cottrell, L., Dunigan, T.: Measuring End-to-end Bandwidth with Iperf using Web100. In: Proc. of PAM (2003)Google Scholar
  2. 2.
    Tang, H., Duan, L., Li, J.: A Performance Monitoring Architecture for IP Videoconferencing. In: Proc. of IPOM (2004)Google Scholar
  3. 3.
    Implementing QoS Solutions for H.323 Videoconferencing over IP, Cisco Systems Technical Whitepaper Document Id: 21662 (2007)Google Scholar
  4. 4.
    ITU-T Recommendation G.114, One-Way Transmission Time (1996)Google Scholar
  5. 5.
    Calyam, P., Sridharan, M., Mandrawa, W., Schopis, P.: Performance Measurement and Analysis of H.323 Traffic. In: Barakat, C., Pratt, I. (eds.) PAM 2004. LNCS, vol. 3015, Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Claypool, M., Tanner, J.: The Effects of Jitter on the Perceptual Quality of Video. In: Proc. of ACM Multimedia (1999)Google Scholar
  7. 7.
    Rix, A., Bourret, A., Hollier, M.: Models of Human Perception, BT Technology Journal 17 (1999)Google Scholar
  8. 8.
    Jha, S., Hassan, M.: Engineering Internet QoS, Artech House Publication (2002), ISBN: 1580533418Google Scholar
  9. 9.
    Markopoulou, A., Tobagi, F., Karam, M.: Loss and Delay Measurements of Internet Backbones, Elsevier Computer Communications (2006)Google Scholar
  10. 10.
    Ciavattone, L., Morton, A., Ramachandran, G.: Standardized Active Measurements on a Tier 1 IP Backbone. IEEE Communications Magazine (2003)Google Scholar
  11. 11.
    Arizona State University Video Trace Library (2007), http://trace.eas.asu.edu
  12. 12.
    Brockwell, P., Davis, R.: Introduction to Time Series and Forecasting. Springer, New York (2002)CrossRefMATHGoogle Scholar
  13. 13.
    NISTnet Network Emulator, http://snad.ncsl.nist.gov/itg/nistnet

Copyright information

© IFIP International Federation for Information Processing 2007

Authors and Affiliations

  • Prasad Calyam
    • 1
  • Mark Haffner
    • 1
  • Eylem Ekici
    • 1
  • Chang-Gun Lee
    • 2
  1. 1.The Ohio State University, Columbus, OH 43210USA
  2. 2.Seoul National University, 151-742Korea

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