Planning and Managing the IPTV Service Deployment

1. Planning and Managing the IPTV Service Deployment Dakshi Agrawal, Mandis S. Beigi, Chatschik Bisdikian, Kang-Won Lee IBM T. J. Watson Research Center, Hawthorne, NY, USA. 10th IFIP/IEEE International Symposium on Integrated Network Management, 2007 Chen Bin Kuo (20077202) Young J. Won (20063292)

2. Outline • Introduction • The IPTV Distribution Model • Problem Formulation • Solution Design • Design of the Planning Tool • Concluding Remarks

3. Introduction • Integration of services over converged networks • Providing the opportunity for legacy players • Emergence of triple-play service offerings • Telephony services companies (TelCos) • Providing services based on the DSLs • Upgrading their network to be able to provide triple-play services

4. Introduction (contd.) • This paper focuses on the emerging deployment of TV and video-on-demand services by TelCos • IPTV can utilize network resources efficiently and facilitate new service features such as: • Multiple views on the same event • Integrated video-on-demand (VoD) - listings for live and VoD programming • Program navigation and search • VCR-like commands

5. Introduction (contd.) • This paper presents: • A model for IPTV service distribution and key parameters be used to analyze the performance • A general framework for planning an IPTV service deployment and management • A solution design for a deployment management tool based on the framework proposed in this paper • Overall issue of IPTV service provisioning

6. The IPTV Distribution Model Client Domain Network Provider Domain Service Provider Domain Quality of Experience (QoE)

7. The IPTV Distribution Model • Residential gateways • Set-top-box (STB) • Distributing various services • Based on the FTTN • Last-mile and second-mile network • (a) DSLAM, (b) routers

8. The IPTV Distribution Model (contd.) • Client Domain • Network Provider Domain • Service Provider Domain • Super Headend (SHE) • Manages and processes all incoming broadcast video feeds and to the downstream • Video Headend Office (VHO) • Typically serves a region or a metropolitan area • Inserts local TV channels and advertisements into the IPTV streams • Video Switching Office (VSO) • Multiplexing video service with other services (VoIP, broadband Internet access)

9. The IPTV Distribution Model (contd.) • Quality of Experience (QoE) • Representing a collection of metrics to reflect the subscribers’ satisfaction • QoE metrics • Video quality • Channel change time (channel zapping time) • Blocking probability for VoD requests • Additional metrics can be supported by the framework

10. Problem Formulation A Model of the IPTV Infrastructure Optimization Problem Formulation

11. A Model of the IPTV Infrastructure • Modeling an IPTV network using a graph consisting of nodes and edges • Link has propagation delay and packet loss rate parameters • Modeling sites and servers as queueing systems • One may substitute more sophisticate models when they become available • Capture a macroscopic behavior of viewers • For example, by the Nielsen ratings [4] • Deriving the channel viewing preference for each community

12. Optimization Problem Formulation • Given an IPTV infrastructure currently serving a set of existing communities • The problem is to fine the way to maximize the number of new subscribers without adding new resources • Observing that the problem can be formulated as a combinatorial optimization problem such as knapsack problem or a bin packing problem • NP-hard • Efficient algorithms exist

13. Solution Design Community Model Channel Zapping Delay Data Server Model Video Quality Models

14. Community Model • Assuming viewing profile of viewers are available to service provider • Define a viewer community to be a collection of viewers • Residing in a geographical proximity and treated as uniform • For each community: • Channel viewing preference: • The VoD content duration statistics: • The viewer request rate vector:

15. Channel Zapping Delay

16. Channel Zapping Delay (contd.) • A viewer in community j switches to channel i • The zapping delay for community j • The overall zapping delay

17. Data Server Model • Adopting the M/M/c/(c+K) queueing model

18. Data Server Model (contd.) • Blocking probability can be solved in queueing system [7] [8] • One may choose to use a more elaborate model– VoD server infrastructure • In [9] [10] for VoD system design also use Markovian queueing models or extensions of these models

19. Video Quality Models • Adopting the moving pictures quality metirc (MPQM) [11] [12] • Representing a numeric score denoting a viewing experience from bad (1) to excellent (5) • A basic human vision model which takes into account the viewers perception of the video • MPQM model:

20. Design of the Planning Tool Software Architecture Algorithmic Structure Case Study – Adding New Markets

21. Software Architecture • Developed as a proof of concept of the proposed framework • Functional diagram

22. Algorithmic Structure • Using a knapsack algorithm to solve the problem • Multiple knapsack problem (MKP): • NP-hard problem • [5] already presented an efficient algorithm for MKP • Relationship • Each community is an item, each IPTV node is a knapsack with certain capacity • Connecting a new community has some value • Cannot directly apply

23. Algorithmic Structure (contd.) • Fitting model to MKP: • Server capacity: • A server typically has a fixed bound for the rate of request • Treating like the weight of the item in MKP • Channel zapping delay: • Using the iterative calculation in (5), we can efficiently test this condition • Service blocking probability: • Easily tested for each sites because it depends on the site parameters • Under Poisson assumption, we can simply update it • Network parameters: • For this parameter, we just need to consider the new community

24. Case Study – Adding New Markets • A service provider has two VHOs near mid size cities that are currently over-provisioned • The service provider tries to serve ten new emerging communities out of these two VHOs

25. Case Study – Adding New Markets (contd.)

26. Concluding Remarks • This paper focused on a framework to aid planning and managing the deployment of IPTV services • The models are used to map a set of external parameters • Service support resources, network nodes and topology, and communities of viewers • Depending on the complexity of the deployment options either exhaustive scans or intelligent scans can be used • Different deployment objectives can be studied through the framework

27. Q & AThanks for your attention!