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

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.

[1]  Jianbo Wang,et al.  The Implementation and Application of IPTV Supported on Pull Mode of P2P , 2008, 2008 International Symposium on Knowledge Acquisition and Modeling.

[2]  Jörg Ott,et al.  On the use of RTP for monitoring and fault isolation in IPTV , 2010, IEEE Network.

[3]  M. Kampmann Predicting IPTV usage : an SEM approach , 2009 .

[4]  Gabriel-Miro Muntean,et al.  Framework for Interactive Personalised IPTV for Entertainment , 2006 .

[5]  Harry Bouwman,et al.  A business model for IPTV service: A dynamic framework , 2008 .

[6]  K. K. Ramakrishnan,et al.  Capacity requirements for on-demand IPTV services , 2011, 2011 Third International Conference on Communication Systems and Networks (COMSNETS 2011).

[7]  Alan F. Smeaton,et al.  Performance-Aware Replication of Distributed Pre-Recorded IPTV Content , 2009, IEEE Transactions on Broadcasting.

[8]  Sheng Wu,et al.  The integration of value-based adoption and expectation-confirmation models: An example of IPTV continuance intention , 2012, Decis. Support Syst..

[9]  Seungjoon Lee,et al.  Modeling user activities in a large IPTV system , 2009, IMC '09.

[10]  Sanggil Kang,et al.  An ontology-based personalized target advertisement system on interactive TV , 2011, 2011 IEEE International Conference on Consumer Electronics (ICCE).

[11]  Munkee Choi,et al.  Analysis on the Mobil IPTV Adoption and Moderator Effect Using Extended TAM Model , 2008, 2008 Fourth International Conference on Networked Computing and Advanced Information Management.

[12]  Taeha Kim,et al.  IPTV in Korea: The Effect of Perceived Interactivity on Trust, Emotion, and Continuous Use Intention* , 2013 .

[13]  Antonio Pescapè,et al.  Traffic analysis of peer-to-peer IPTV communities , 2009, Comput. Networks.

[14]  Gyu Myoung Lee,et al.  Web-Based Personalized IPTV Services over NGN , 2008, 2008 Proceedings of 17th International Conference on Computer Communications and Networks.

[15]  Eduardo Barrére,et al.  A platform for difusion interactive multimedia content: an approach focused on IPTV system and broadcasting digital television system , 2008, EATIS.

[16]  Dong-Hee Shin,et al.  RETRACTED: Potential user factors driving adoption of IPTV. What are customers expecting from IPTV? , 2007 .

[17]  K. K. Ramakrishnan,et al.  Understanding couch potatoes: measurement and modeling of interactive usage of IPTV at large scale , 2011, IMC '11.