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)

DPNM Lab.

Introduction The IPTV Distribution Model Problem Formulation Solution Design Design of the Planning Tool Concluding Remarks

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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

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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

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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

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a) Client Domainb) Network Provider Domainc) Service Provider Domaind) Quality of Experience (QoE)

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• 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

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a) Client Domainb) Network Provider Domainc) 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)

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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

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a) A Model of the IPTV Infrastructureb) Optimization Problem Formulation

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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

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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

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a) Community Modelb) Channel Zapping Delayc) Data Server Modeld) Video Quality Models

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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:

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A viewer in community j switches to channel i

The zapping delay for community j

The overall zapping delay

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Adopting the M/M/c/(c+K) queueing model

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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

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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:

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a) Software Architectureb) Algorithmic Structurec) Case Study – Adding New Markets

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DPNM Lab.

Developed as a proof of concept of the proposed framework

Functional diagram

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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

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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

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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

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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

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