Popularity Prediction of Videos in IPTV Systems

  • Reyhaneh Ghavami Department of Computer Engineering and Information Technology University of Kurdistan, Sanandaj, Iran
  • Alireza Abdollahpouri Department of Computer Engineering and Information Technology, University of Kurdistan, Sanandaj, Iran.
  • Zhila Bahrami Department of Computer Engineering and Information Technology, University of Kurdistan, Sanandaj, Iran.
  • Parham Moradi Department of Computer Engineering and Information Technology, University of Kurdistan, Sanandaj, Iran.

Abstract

Video on demand (VoD) service in IPTV is a bandwidth-hungry application. It has been argued that the distribution of popularity of videos can be well measured using a Zipf-like distribution in which top 10% of the videos account for nearly 90% of requests. In this article, we propose a neural network based method to predict the popularity of videos in an IPTV system. The popularity prediction can be used by service providers for video placement in content delivery systems or hierarchical servers and hence it can lead to bandwidth save. Simulation-based performance evaluation of our proposed method confirms a significant accuracy in the prediction of the popularity of the videos.

References

[1] IMS Research. A Global Market Analysis, 2011 Edition.
[2] http://www.att.com/u-verse (last visited: Feb. 2014)
[3] http://www.Telekom.de/Entertain (last visited: Feb. 2014)
[4] http://www.telefonica.com (last visited: Feb. 2014)
[5] S. Songbo, H. Moustafa, and H. Afifi, "Advanced IPTV services personalization through context-aware content recommendation" Multimedia, IEEE Transactions on 14.6 (2012): pp. 1528-1537.
[6] K. Marko, and M. Bjelica, "Context-aware personalized program guide based on neural network," Consumer Electronics, IEEE Transactions on 58.4 (2012): pp. 1301-1306.
[7] J. Li, S. Hong and S. Xia, “Neural Network Based Popularity Prediction For IPTV System” J. of Networks, V.7 No.12 Dec. 2012.
[8] A. Abdollahpouri, B. E. Wolfinger, J. Lai, and C. Vinti, "Elaboration and formal description of IPTV user models and their application to IPTV system analysis," in Proceedings of MMBnet 2011 Workshop, Hamburg, Germany, Sept. 2011.
[9] Hagan, Martin T., Howard B. Demuth, and Mark H. Beale. Neural network design. Boston: Pws Pub., 1996.
Published
2015-08-31
How to Cite
GHAVAMI, Reyhaneh et al. Popularity Prediction of Videos in IPTV Systems. Journal of University of Human Development, [S.l.], v. 1, n. 3, p. 385-389, aug. 2015. ISSN 2411-7765. Available at: <https://journals.uhd.edu.iq/index.php/juhd/article/view/650>. Date accessed: 14 june 2021. doi: https://doi.org/10.21928/juhd.v1n3y2015.pp385-389.
Section
Articles