Internet television (IPTV) is rapidly gaining popularity and is being widely deployed in content delivery networks on the Internet. In order to proactively deliver optimum user quality of experience (QoE) for IPTV, service providers need to identify network bottlenecks in real time. In this paper, we develop psycho-acoustic-visual models that can predict user QoE of multimedia applications in real time based on online network status measurements. Our models are neural network based and cater to multi-resolution IPTV applications that include QCIF, QVGA, SD, and HD resolutions encoded using popular audio and video codec combinations. On the network side, our models account for jitter and loss levels, as well as router queuing disciplines: packet-ordered and time-ordered FIFO. We evaluate the performance of our multi-resolution multimedia QoE models in terms of prediction characteristics, accuracy, speed, and consistency. Our evaluation results demonstrate that the models are pertinent for real-time QoE monitoring and resource adaptation in IPTV content delivery networks. 1. Introduction Internet television (IPTV) is rapidly gaining popularity and is expected to reach over 50 million households in the next two years [1]. The key drivers of IPTV deployment are its cost-savings when offered with VoIP and Internet service bundles, increased accessibility by a variety of mobile devices, and compatibility with modern content distribution channels such as social networks and online movie rentals. In spite of the best-effort quality of service (QoS) of the Internet, IPTV service providers have yet to deliver the same or better user quality of experience (QoE) than traditional TV technology. Consequently, they need to understand and balance the trade-offs involved with various factors that affect IPTV deployment (shown in Figure 1). The primary factors are: user (video content, display device), application (codec type, encoding bit rate), and network (network health, router queuing discipline) factors. Figure 1: IPTV system components. User factors relate to the temporal and spatial activity level of the video content. For example, a news clip has low activity level, whereas a sports clip has high activity level. Also, based on mobility and user-context considerations, the display device could support a subset of video resolutions such as quarter common intermediate format (QCIF), quarter video graphics array (QVGA), standard definition (SD), or high definition (HD). Typically QCIF and QVGA with video resolutions of 176 × 144 and 320 × 240, respectively, are
References
[1]
E. Jopling and A. Sabia, “Forecast: IPTV subscribers and service revenue, worldwide, 2004–2010,” Gartner IPTV Market Report, 6 pages, 2006.
[2]
J. Klaue, B. Rathke, and A. Wolisz, “EvalVid—A framework for video transmission and quality evaluation,” in Proceedings of the 13th International Conference on Modelling Techniques and Tools for Computer Performance Evaluation, pp. 255–272, 2003.
[3]
ITU-T Recommendation J.144, “Objective perceptual video quality measurement techniques for digital cable television in the presence of a full reference,” 2001.
P. Calyam, M. Haffner, E. Ekici, and C. G. Lee, “Measuring interaction QoE in Internet videoconferencing,” IEEE MMNS, vol. 4787, pp. 14–25, 2007.
[6]
G. M. Muntean, P. Perry, and L. Murphy, “Subjective assessment of the quality-oriented adaptive scheme,” IEEE Transactions on Broadcasting, vol. 51, no. 3, pp. 276–286, 2005.
[7]
X. Lu, S. Tao, M. Zarki, and R. Guerin, “Quality-based adaptive video over the Internet,” in Proceedings of the CNDS, 2003.
[8]
M. Ghanbari, D. Crawford, M. Fleury, et al., “Future performance of video codecs,” in Ofcom Research Report (SES2006-7-13), 2006.
[9]
X. Hei, C. Liang, J. Liang, Y. Liu, and K. W. Ross, “A measurement study of a large-scale P2P IPTV system,” IEEE Transactions on Multimedia, vol. 9, no. 8, pp. 1672–1687, 2007.
[10]
M. Claypool and J. Tanner, “The effects of jitter on the perceptual quality of video,” in Proceedings of the ACM Multimedia, pp. 115–118, New York, NY, USA =, 1999.
[11]
Y. Won and M. Choi, “End-User IPTV traffic measurement of residential broadband access networks,” in Proceedings of the IEEE Workshop on End-to-End Monitoring Techniques and Services, pp. 95–100, Salvador, Bahia, Brazil, 2008.
[12]
S. Winkler and R. Campos, “Video quality evaluation for Internet streaming applications,” Human Vision and Electronic Imaging, vol. 3, pp. 104–115, 2003.
[13]
S. Mohamed and G. Rubino, “A study of real-time packet video quality using random neural networks,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 12, no. 12, pp. 1071–1083, 2002.
[14]
M. H. Pinson and S. Wolf, “A new standardized method for objectively measuring video quality,” IEEE Transactions on Broadcasting, vol. 50, no. 3, pp. 312–322, 2004.
[15]
“The E-Model: a computational model for use in transmission planning,” ITU-T Rec. G.107, 1998.
[16]
A. P. Markopoulou, F. A. Tobagi, and M. J. Karam, “Assessment of VoIP quality over Internet backbones,” in Proceedings of the IEEE Infocom, pp. 150–159, June 2002.
[17]
S. Tao, J. Apostolopoulos, and R. Guérin, “Real-time monitoring of video quality in IP networks,” IEEE/ACM Transactions on Networking, vol. 16, no. 5, pp. 1052–1065, 2008.
[18]
F. Massidda, D. D. Giusto, and C. Perra, “No reference video quality estimation based on human visual system for 2.5/3G devices,” in Proceedings of the SPIE and T Electronic Imaging, pp. 168–179, January 2005.
[19]
H. J. Kim and S. G. Choi, “A study on a QoS/QoE correlation model for QoE evaluation on IPTV service,” in Proceedings of the 12th International Conference on Advanced Communication Technology (ICACT '10), pp. 1377–1382, February 2010.
[20]
V. Menkovski, A. Oredope, A. Liotta, and A. Cuadra, “Predicting quality of experience in multimedia streaming,” in Proceedings of the 7th International Conference on Advances in Mobile Computing and Multimedia, (MoMM '09), pp. 52–59, December 2009.
[21]
S. Balasubramaniam, J. Mineraud, P. McDonagh, et al., “An evaluation of parameterized gradient based routing with QoE monitoring for multiple IPTV providers,” IEEE Transactions on Broadcasting, vol. 57, no. 2, pp. 183–194, 2011.
[22]
H. Demuth and M. Beale, Matlab Neural Network Toolbox User’s Guide, The MathWorks, 2000.
[23]
A. Takahashi, D. Hands, and V. Barriac, “Standardization activities in the ITU for a QoE assessment of IPTV,” IEEE Communications Magazine, vol. 46, no. 2, pp. 78–84, 2008.
[24]
“VLC media player,” http://www.videolan.org/vlc.
[25]
S. Hemminger, “Netem—Emulating real networks in the lab,” in Proceedings of the Linux Conference, Australia, 2005.
[26]
J. Bellardo and S. Savage, “Measuring packet reordering,” in Proceedings of the 2nd ACM SIGCOMM Internet Measurement Workshop (IMW '02), pp. 97–105, November 2002.
[27]
P. Calyam, M. Sridharan, W. Mandrawa, and P. Schopis, “Performance measurement and analysis of h.323 traffic,” in Proceedings of the Passive and Active Measurement Workshop, vol. 3015, pp. 137–146, 2004.
[28]
T. Friedman, R. Caceres, and A. Clark, “RTP control protocol extended reports (RTCP XR),” IETF RFC 3611, 2003.
