IPTV Bandwidth Demands in Metropolitan Area Networks

1. IPTVBandwidth Demands in Metropolitan Area Networks Jesse E. Simsarian and Marcus Duelk Bell Laboratories, Alcatel-Lucent, Holmdel, NJ, jesses@alcatel-lucent.com 15th IEEE Workshop on Local and Metropolitan Area Networks, 2007 Chen Bin Kuo (20077202) Young J. Won (20063292)

2. Outline • Introduction • Data and Voice Traffic • IPTV Network Architecture • VoD Content and User Behavior • Caching of Content • Main Traffic • Conclusion

3. Introduction • In the paper, authors analyze the bandwidth requirements in Metropolitan Area Networks (MANs) for providing IPTV services. • Developing a model of IPTV network to determine the optimum location of the cached video content. • Finding out the effect upon MAN traffic by users simultaneously requesting VoD streams

4. Introduction (contd.) • Applying previous work on content delivery to a realistic metropolitan area work (MAN) • Previous work: unicast content delivery [1][2] , proxy server method [3], and multicast groups [4][5] • Considering a service provider is optimizing content delivery • Assuming a small amount of buffering at the client’s set-top box • Delivery stream is viewed nearly in real time • Do not consider VoD multicast [4]

5. Introduction (contd.) • Developed a cost model of the total network including: • The servers that cache the on-demand video • Costs associated with data switching and transport • Reporting the results about future MAN traffic

6. Data and Voice Traffic Voice Traffic Data Traffic

7. Data and Voice Traffic • Voice, video, and data traffic • Different burstiness and flow durations • Different QoS requirement • Network operations and networking equipment will be affected by the future traffic mix

8. Voice Traffic Estimation in the MAN • Flat over the next years • Estimated by market studies forecasting [8] • Fixed landlines (PSTN), VoIP, mobile phone lines

9. Data Traffic Estimation in the MAN • Download rate during peak hours (6-10 p.m.) • Studies investigating the Internet usage [10][11] • Traffic pattern of more than twenty Internet exchange points (IXPs) worldwide [12]

10. Data Traffic Estimation in the MAN (contd.) • Forecasting • Market study from 2005 to 2009 in Western Europe [13] • Predicting annual growth rate of roughly 55%with an increase in the business segment and a growth decline in the consumer market • Summarize [12][14][15][16][17] to derive forecasting numbers • Using lower bound and upper bound to represent high uncertainty • Including P2P traffic to be data traffic

11. IPTV Network Architecture VoD Content and User Behavior

12. IPTV Network Architecture • Super headend (SHE) • Video hub offices (VHOs) over the wavelength division multiplexed (WDM) national core • Contain VoD servers • Video source offices (VSOs) also contain VoD servers that cache the more popular content • Central offices (COs)

13. IPTV Network Architecture (contd.)

14. Cost Model • Packet routing and switching costs for VoD traffic • CEIF : the cost per bandwidth of Ethernet interfaces • BVHO: the bandwidth of traffic to the VHO server cache • HVHO : the number of packet routing hops this traffic undergoes • Btotal : the total quantity of VoD traffic • Htotal : the number of packet hops that all of the VoD traffic undergoes

15. Cost Model (contd.) • The cost of the TDM switching and WDM transport is : • The cost for the VoD servers comes from the cost to store films on disk and the streaming interfaces from the video servers : • Cstorage is the cost per film for disk storage, and R is the number of films stored at the VSO. • NVSO is the number of VSO nodes, F is the total number of films offered, Cstream is the cost per VoD stream, and Nsessions is the number of simultaneous VoD sessions

16. VoD Content and User Behavior • The authors believe that future VoD offerings will approach the number titles offered • Today’s Netflix DVD mail-delivery service • A catalog of about 60,000 films [19] • Applying Zipf distribution

17. Caching of Content VoDConcurrency [% of Households] Percent Cached at VSO Relative Cost & Percent Cached at VSO Optimum Caching

18. Caching of Content

19. Caching of Content (contd.)

20. Caching of Content (contd.) • Using cost model to determine Ro, the optimum fraction of content stored at the VSOs.

21. Caching of Content (contd.) • Cost curve for • (a) a low concurrency of 1% • (b) a higher concurrency of 10% Intuitively speaking, when the VoD usage becomes high, the content should be delivered closer to the end user

22. Optimum Caching

23. MAN Traffic

24. MAN Traffic • (a) the VoD traffic generated in the MAN • (b) VoD portion of total MAN traffic • The voice and data traffic are from the projections of Fig. 1-2 for 2010. • Error bars that come form upper and lower bounds of data traffic

25. Conclusion • The authors analyzed the bandwidth requirements in the MAN for IPTV systems. • They find that the bandwidth depends on where VoD content caching and video stream delivery is located. • The proposed cost model gives the optimal location for the content, and from that they determine that future networks could have a large percentage of real-time video traffic.

26. Q & AThanks for your attention!