1. 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. DPNM Lab.  Introduction  The IPTV Distribution Model  Problem Formulation  Solution Design  Design of the Planning Tool  Concluding Remarks 9/11/20152

3. DPNM Lab. 9/11/20153  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. DPNM Lab.  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 9/11/20154

5. DPNM Lab.  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 9/11/20155

6. a) Client Domain b) Network Provider Domain c) Service Provider Domain d) Quality of Experience (QoE) 9/11/20156

7. DPNM Lab. 9/11/20157 Residential gateways Set-top-box (STB) Residential gateways Set-top-box (STB) Distributing various services Based on the FTTN Last-mile and second-mile network (a) DSLAM, (b) routers Distributing various services Based on the FTTN Last-mile and second-mile network (a) DSLAM, (b) routers

8. DPNM Lab. a) Client Domain b) Network Provider Domain c) Service Provider Domain 1.Super Headend (SHE)  Manages and processes all incoming broadcast video feeds and to the downstream 2.Video Headend Office (VHO)  Typically serves a region or a metropolitan area  Inserts local TV channels and advertisements into the IPTV streams 3.Video Switching Office (VSO)  Multiplexing video service with other services (VoIP, broadband Internet access) 9/11/20158

9. DPNM Lab. d) 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 9/11/20159

10. a) A Model of the IPTV Infrastructure b) Optimization Problem Formulation 9/11/201510

11. DPNM Lab.  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 9/11/201511

12. DPNM Lab.  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 9/11/201512

13. a) Community Model b) Channel Zapping Delay c) Data Server Model d) Video Quality Models 9/11/201513

14. DPNM Lab.  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: 9/11/201514

15. DPNM Lab. 9/11/201515

16. DPNM Lab.  A viewer in community j switches to channel i  The zapping delay for community j  The overall zapping delay 9/11/201516

17. DPNM Lab.  Adopting the M/M/c/(c+K) queueing model 9/11/201517

18. DPNM Lab.  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 9/11/201518

19. DPNM Lab.  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: 9/11/201519

20. a) Software Architecture b) Algorithmic Structure c) Case Study – Adding New Markets 9/11/201520

21. DPNM Lab.  Developed as a proof of concept of the proposed framework  Functional diagram 9/11/201521

22. DPNM Lab.  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 9/11/201522

23. DPNM Lab.  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 9/11/201523

24. DPNM Lab.  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 9/11/201524

25. DPNM Lab. 9/11/201525

26. DPNM Lab. 9/11/201526  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. 9/11/201527