Call for Paper - August 2019 Edition
IJCA solicits original research papers for the August 2019 Edition. Last date of manuscript submission is July 20, 2019. Read More

Modelling and Analysis of IPTV Usage Patterns for Improving Quality of Service

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
P. L. Srinivasa Murthy, T. Venu Gopal
10.5120/ijca2016910268

Srinivasa P L Murthy and Venu T Gopal. Modelling and Analysis of IPTV Usage Patterns for Improving Quality of Service. International Journal of Computer Applications 144(4):22-31, June 2016. BibTeX

@article{10.5120/ijca2016910268,
	author = {P. L. Srinivasa Murthy and T. Venu Gopal},
	title = {Modelling and Analysis of IPTV Usage Patterns for Improving Quality of Service},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2016},
	volume = {144},
	number = {4},
	month = {Jun},
	year = {2016},
	issn = {0975-8887},
	pages = {22-31},
	numpages = {10},
	url = {http://www.ijcaonline.org/archives/volume144/number4/25168-2016910268},
	doi = {10.5120/ijca2016910268},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Of late IPTV usage is growing rapidly as the viewers are interested to watch stored videos interactively over IP. The bursts in user demands for on-demand content can cause unexpected burden on the content dissemination infrastructure. Stated differently the usage dynamics of video content has its impact on the responsiveness, bandwidth and server. Especially it is non-trivial to solve the problem when number of subscribers is very huge. Here comes the need for modelling and the usage patterns of IPTV and analysing it for making important strategies for server to cope with bursts of subscriber requests. The discovery of usage patterns also considers periods of usage such as week day and week end. In this paper a framework is proposed that can help in modelling and analysis of IPTV usage patterns. The video streaming control events are also considered for the modelling. Characterization of stream control events using a finite state machine with and estimated Markov chain is made. The proposed modelling is validated with traces of operational IPTV environment in large scale.

References

  1. Tongqing Qiu,Zihui Ge,Seungjoon Lee. (2009). Modeling User Activities in a Large IPTV System. ACM, p.45-56.
  2. Tung-Ching Lin a, Sheng Wu b, Jack Shih-Chieh Hsu a, Yi-Ching Chou. (2012). The integration of value-based adoption and expectation–confirmation models: An example of IPTV continuance intention. Elsevier. 54 , p.20-30.
  3. Harry Bouwman, Meng Zhengjia, Patrick van der Duin and Sander Limonard. (2008). A business model for IPTV service: a dynamic framework. ACM, p.20-30.
  4. Cristina Hava Muntean,Gabriel-Miro Muntean. (2005). Framework for Interactive Personalised IPTV for Entertainment. ACM, p.12-19.
  5. Yonghee Shin , Hyori Jeon, Munkee Choi. (2008). Analysis on the Mobil IPTV Adoption and Moderator Effect Using Extended TAM Model.IEEE, p.45-56.
  6. Jun Kyun Choi, Gyu Myoung Lee and Hyo Jin Park. (2008). Web-based Personalized IPTV Services over NGN. IEEE, p.901-1002.
  7. Dong Hee Shin. (2007). Potential user factors driving adoption of IPTV. What are customers expecting from IPTV. Elsevier. 74, p.45-56.
  8. Jongwoo Kim & Sanggil Kang. (2013). An ontology-based personalized target advertisement system on interactive TV. Springer-Verlag London Limited, p.20-30.
  9. Matej Zajc, Kemal Alič, Irena Battelino, Jurij Tasič. (2006). Challenges of Interactive Digital Television for t-Learning. ACM, p.12-19.
  10. Pat Diminico, Vijay Gopalakrishnan, Rittwik Jana, K.K. Ramakrishnan. (2011). Capacity Requirements for On-Demand IPTV Services. IEEE, p.901-1002.
  11. [Thomas Silverston , Olivier Fourmaux , Alessio Botta , Alberto Dainotti , Antonio Pescapé , Giorgio Ventre , Kavé Salamatian. (2009). Traffic analysis of peer-to-peer IPTV communities. Elsevier, p.20-30.
  12. Vijay Gopalakrishnan, Rittwik Jana, K. K. Ramakrishnan. (2011). Understanding Couch Potatoes: Measurement and Modeling of Interactive Usage of IPTV at large scale. ACM, p.901-1002.
  13. Christian Riede,Oliver Friedrich, Robert Seeliger, Stefan Arbanowski. (2008). Interactive IMS-based IPTV. ACM, p.90-101.
  14. Jo Groebel. (2009). Internet Television. ACM, p.20-30.
  15. João Benedito dos Santos Junior,Iran Calixto Abrão, Eduardo Barrére,Paulo Muniz de Ávila, Gabriel Massote Prado. (2008). A Platform for Difusion Interactive Multimedia Content: An Approach Focused on IPTV System and Broadcasting Digital Television System.Elsevier, p.80-86.
  16. Seung-Bum Lee, Student Member, IEEE, Gabriel-Miro Muntean, Member, IEEE, and Alan F. Smeaton, Member, IE. (2009). Performance-Aware Replication of Distributed Pre-Recorded IPTV Content. IEEE, p.12-19.
  17. Mingshan Ma, Yanyan Ma, Jianbo Wang. (2008). The Implementation and Application of IPTV Supported on Pull Mode of P2P.International Symposium on Knowledge Acquisition and Modeling, p.45-56.
  18. Ali C. Begen, Cisco. (2010). On the Use of RTP for Monitoring and Fault Isolation in IPTV. IEEE, p.12-19.
  19. Matthias W. Kampmann. (2011). Predicting IPTV usage: An SEM Approach. ACM, p.45-56.
  20. Geena Shin, Joong-Ho Ahh, Taeha Kim. (2013). IPTV in Korea: The Effect of Perceived Interactivity on Trust, Emotion, and Continuous Use Intention. Asia Pacific Journal of Information Systems. 23 (3), p.20-30.

Keywords

IPTV, modeling and analysis, finite state machine, markov chain