Peer to peer vs ip multicast comparing approaches to iptv streaming based on tv channel popularity
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Peer-to-Peer vs. IP Multicast Comparing Approaches to IPTV Streaming Based on TV Channel Popularity. Alex Bikfalvi  Jaime García-Reinoso  Iván Vidal Francisco Valera  Arturo Azcorra. Commercial-grade IPTV. How some telcos stream IPTV?. IPTV broadcast server. Backbone network.

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Peer to peer vs ip multicast comparing approaches to iptv streaming based on tv channel popularity

Peer-to-Peer vs. IP MulticastComparing Approaches to IPTV Streaming Based on TV Channel Popularity

Alex Bikfalvi  Jaime García-Reinoso  Iván Vidal Francisco Valera  Arturo Azcorra


Commercial grade iptv
Commercial-grade IPTV

  • How some telcos stream IPTV?

IPTV broadcast server

Backbone network

IP multicast (static)

DSLAM

Customer premise

DSLAM

xDSL

DSLAM

xDSL

xDSL

Customer premise

IGMP: 1-2 channels

xDSL

ADSL router

Set-top box

Customer premise

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TV set


Motivation
Motivation

  • Most deployment are walled-gardens

    • Multicast has been the preferred technical solution

  • Current/possible future tends…

    • Next generation networks, open to third-party providers

    • Studies show that over 90 % of channels are watched by 20% of subscribers

    • Semi-interactive techniques: NVoD

    • User generated content

  • Possible issues for the telcos

    • Is it still affordable to use multicast?

    • Even for very unpopular channels?

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What are we doing
What are we doing

Let’s compare IP multicast with an alternative: Peer-to-Peer

  • Why P2P?

    • Telcos can leverage their set-top boxes to form a P2P overlay

  • Main question

    • How the TV channel popularity affects the difference in performance

  • Dimensions of our analysis

    • Bandwidth utilization

    • Multicast scalability

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Setting up the foundation

The streaming

TV watching

The network

Setting up the foundation

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Streaming scenario
Streaming scenario

  • Hybrid: IP multicast and P2P-based unicast

    • 100 TV channels

  • P2P-based unicast

    • Set-top boxes (STBs) are peers

    • Channel stream is pulled/pushed from/by other STB(s)

    • The head-end server is a last resort

g

N–g

IP multicast connections

(P2P) unicast connections

N TV channels

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P2p overlay
P2P overlay

  • A P2P algorithm handles peer discovery

    • I.e. another STB receiving the same channel

  • Another dimension to the problem: locality

  • Algorithm effectiveness: P2P ratio

Number of peers connected to peers

Number of peers

Number of peers connected to the server

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Watching tv
Watching TV

  • Modeling the user behavior

    • How long a user watches a TV channel: channel holding time (CHT)

    • TV channel popularity

    • TV channel zappingprobability

    • TV channel number of viewers

  • The model

    • Input: CHT and popularity

    • Output: zapping probability

    • 10000 users and limited number of popularity levels

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Popularity model
Popularity model

  • What is the channel popularity?

    • How often users arrive/leave

    • How long they watch the channel

    • It sums the CHTs of all viewers during the observation period

    • The popularity of all channels:

Number of viewers

Observation period

Time

Popularity:

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Zapping probability
Zapping probability

  • The probability of changing to a TV channel

    • Relationship with popularity

j

n

Sufficiently large observation period and all channels have the same probability distribution of the channel holding time

i

k

m

l

Popularity of channel i

Zapping probability of channel i

Popularity of all channels

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Viewers
Viewers

  • The average number of users watching a channel

Number of viewers

Observation period

Time

Popularity:

Number of users (10000)

Observation period

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Our model
Our model

  • Define channel popularity levels

    • Abstract, not based on a measurement

    • The effects to be easy identifiable

    • If possible, popularity to translate in easy zapping decisions

  • CHT: measurement study (Cha et al.)

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Network topology
Network topology

  • Access network like DSL

    • One link (hop) from backbone to customer premise

  • Backbone network using BRITE

    • 100 routers / 50 edge routers

    • Ratio edges-to-nodes (m): 1, 2, 3, 4

    • Average path length between two nodes: lu

    • Average multicast tree size from a source to a group of g nodes: lm= f(g)

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More on multicast trees
More on multicast trees

  • Tree size vs. group size

    • When g much smaller than the number of edge routers: power-law (Chuang and Sirbu)

    • When g much larger than the number of edge routers:constant

Tree size (lm)

Here we explore the P2P alternative

Here IP multicast is really worth the buck

Group size (g)

Number of edge routers

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So for our backbone
So for our backbone…

  • Set of measurements:

    • Random sources

    • Random groups

Worse connected network

Saturation

Better connected network

Power-law

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Bandwidth utilization

Analytical estimation

Bandwidth utilization

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The problem
The problem

  • Input

    • IPTV streaming: multicast & P2P

      • Random peers, preferred peers, locality optional: ρ

    • Watching TV: CHT, i, pi, vi

    • Network topology: lu, lm

  • Output

    • Average bandwidth utilization: B

    • Bandwidth of one stream: B0

Core

mcast

Core

ucast

Access

Core

Access

up

Access

down

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Access downstream
Access downstream

  • The easy solution:

    • All U users watch a TV channel

  • The not so-easy solution

    • As an exercise: sum for all channels

Does not depend on the channel popularity

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Access upstream
Access upstream

  • It depends only on the channels using P2P

    • g channels IP multicast / N – g channels using P2P

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Core unicast
Core unicast

  • Only for TV channels that use P2P

  • Depending on the average path length: lu

  • Locality?

Ratio of viewers using the server

Ratio of viewers using a peer

P2P path length

P2 server path length

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Core multicast
Core multicast

  • Only for TV channels that user IP multicast

  • Depending on the average tree size: lm

Depends on the group size, i.e. channel popularity (number of viewers)

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Putting everything together

Network

Popularity

Overlay

Locality

Putting everything together

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Let s sum up
Let’s sum up

  • The bandwidth has 4 components

    • Multicast channels: access downstream & core multicast

    • Unicast channels: access down/up & core unicast

  • Intuitive result

Bandwidth (B)

Access down

Access up

Core ucast

Core mcast

Number of channels using multicast (g)

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Network effect
Network effect

  • Two groups of channels: 20 popular & 80 unpopular

  • Choose g between 0 and 100

  • For every network topology (m)

Better connected network, less bandwidth

Q1 = 0.6

Multicast is better, especially for non-popular channels

Q2 = 0.4

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Network effect1
Network effect

  • Same for 3 groups of channels

    • 20 very popular, 30 average, 50 unpopular

Q1 = 0.4

Q2 = 0.3

Q3 = 0.3

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Popularity effect
Popularity effect

  • Increase the popularity of the popular channels

    • 20 popular channels, 80 unpopular channels

Increasing popularity of popular channels

Well well well… we don’t gain so much by using multicast

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Overlay effect
Overlay effect

  • For unicast channels: use a peer or use the server?

    • Use a peer: scalable, distributed system

    • Use the server: centralized system

  • Let’s play with ρ

Using the server, we cut the upstream in the access network

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Locality effect
Locality effect

  • Let’s pull the ace card for P2P: locality

    • P2P cannot cut from the access upstream: we need the upstream

    • P2P can cut from the distance between peers: the server is fixed!

no locality

locality

What we loose in the upstream for 100% P2P we can gain with a locality factor of 0.8

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Bandwidth vs popularity
Bandwidth vs. popularity

  • For one channel, we compare unicast and multicast

    • Changing the channel popularity

    • We have 10000 users, 100 channels: the average popularity is 100 users/channel

Here: multicast

These values are for a worse P2P case!

Cold channels

Hot channels

Here: we can choose

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Did we get the equations right

Simulation results

Did we get the equations right?

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The software
The software

  • Put everything in a computer simulation

    • Test the an actual P2P overlay

    • User behavior over time: channel holding time

  • Objectives

    • Verify our equations (whether the averages hold)

    • Verify our assumptions (can ρ describe the peer discovery decisions)

    • Determine realistic values for ρ and λ (locality)

  • We implemented the for P2P algorithms

    • Random peer, with and without locality

    • Preferred peer, with and without locality

      • Preferred peer: constraints on bandwidth, duration on the TV channel (less churn), distance from the server, etc.

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Simulation data
Simulation data

Although the design of the P2P overlay may affect the locality factor we can obtain

The equations approximate well the bandwidth utilization

We ensure plenty resources(each peer can server at least 2 other peers): ρ can be high

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Summing up
Summing up

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Multicast scalability
Multicast scalability

  • Scalability has been recognized and studied

    • There is no natural way of consolidating multicast entries

    • There are some solutions on aggregation but not uniformly implemented

    • We acknowledge that scalability is only a performance problem

What we gain in terms of bandwidth

What we loose in terms of scalability

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Is there room for p2p
Is there room for P2P?

  • In current IPTV deployments there are many unpopular channels (few users per channel)

    • But their number is limited: hundreds

    • What happens for many more TV channels?

    • Third party service providers

    • User generated content

  • Of course, a definitive answer depends on…

    • Will the telcos leverage their set-top boxes for this services

    • Cost estimation (pricing is not difficult even for multicast alone)

  • We only examined bandwidth and scalability

    • Other considerations (delay)

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Thanks

Thanks

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