Resource Assignment for Integrated Services in Wireless ...

Resource Assignment for IntegratedServices in Wireless Multimedia Networks

Victor [email protected]

http://research.microsoft.com/~bahl

Microsoft ResearchRedmond, Washington

August 8, 1997

August 8, 1997 Victor Bahl

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1 Introduction and Problem Description

2 Bandwidth Allocation, Reservation, and Utilization→ Connection level Intra-Frame Statistical Multiplexing

3 Bandwidth Partitioning→ Priority Sharing with Restrictions (PSR)

→ The Smart Allocate Algorithm

4 Conclusions

Current Research Activities

End NodeOptimizations

System LevelOptimizations


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l Different broadband sources (traffic classes) require differentamounts of bandwidth and have different transmission priorities

• Bandwidth: Video > Voice > Data

• Priorities: Voice > Video > Data

l Challenge for the network designer: Develop techniques that can assign bandwidth to the different traffic classes

in manner such that• a guarantee on the level of service can be provided to a portion of the

time-critical traffic classes, and

• the needs of the system (i.e. maximize the # of supported connections)are balanced against the needs of the applications

(Bandwidth prediction and reservation for bursty sources such as VBR video isan unsolved problem, so how do we allocate bandwidth for such sources)


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Adapt the application:– Use an appropriate (joint source-channel) video codec

• very low bit rate with high average PSNR and acceptableframe rate (subband based, MPEG-4, H.263L etc.)

– Use a CBR voice connection (e.g GSM speech CODEC)

Adapt the communication layer:– Carry out appropriate resource management (bandwidth

reservation, allocation and utilization)– Implement a “smart” bandwidth partitioning strategy

– Implement a complimentary MAC protocol


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QuestionCan performance guarantees be provided to VBR video withoutsignificantly under-utilizing the bandwidth?

AnswerYes! Use Intra-frame statistical multiplexing


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Text

VideoSource

Region 1 /Subband 1

Region 2 /Subband 2

Region n /Subband n

MUX

MUX

Audio Source

Data Source

Laptop

Connection-level Multiplexing

System-level multiplexing

Laptop

LaptopLaptop


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Primary Region Usage

0

5

10

15

20

25

30

0 125 250 375 500 625 750 875

Time (Seconds)

Tot

al B

its G

ener

ated

(M

bits

)

Statistical Multiplexing within a Frame

0

0.2

0.4

0.6

0.8

1

0 3 6 9 12 15 18 21 24 27

Time (Seconds)T

otal

Bits

(M

bits

)

Cumulative Arrivals Reserved Bandwidth

Predicted Demand

Actual Demand

Bandwidth usage with and without intra-frame statistical multiplexing


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Possible Strategies:+ Complete Sharing

+ Complete Partitioning (or Mutually Restricted Access)+ Partitioning with Priority Borrowing (or Partial sharing)

Packet Voice

DataPacket Video

Packet Voice

DataPacket Video

Packet Voice

DataPacket Video

Strategy 1: Complete Sharing (CS) Strategy 2: Complete Partitioning (CP) Strategy 3: Priority Sharing (PS)


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CP CS PS• Blocking level for

each class easy toadjust

• Blocking level amongclasses notadjustable

• May be tunable tovariable blockingrequirements

• Complete Protectionfrom overload ofother classes

• No protection fromoverload of otherclasses

• May offer protectionagainst overloadfrom other classes

• Requires too muchbandwidth

• Better Bandwidthusage than CP

• More efficientbandwidth usagethan CP and CS


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Packet VoiceData

Packet Video

Contend Reserved

Moving boundary Moving boundary

Fixed boundary

Data Voice Video-Dynamic

Video-Static

Data - BP BP BPVoice B - B BP

Video-Dynamic B BP - BP

Video-Static X X X -

B --- Borrow BP --- Borrow, but Preemption possible

RequesterReserved


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Voice + Data Traffic

Voice Traffic Load = 30 ErlangsData Traffic Load = 10 Erlangs

Video Traffic

Compression = Region-based H.263Avg. Frame Rate = 7.5 HzAvg. Video Bit rate = 24 kbpsImage Dimensions = QCIF (176 x 144)# of Regions/Image = 5Peak for primary Region = 20 kbpsAvg. PSNR = 30.4 dB

Bandwidth Partitioning and VBR Video

0

5

10

15

20

25

30

35

5 25 45 65 85 105

125

Offered Video Traffic Load (Erlangs)

Ave

rage

PS

NR

PB

PSR

CS


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voice = 60 erlangs

data = 10 erlangs

Bandwidth Partitioning and VBR Video

0

5

10

15

20

25

30

35

5 25 45 65 85 105 125Offered Video Traffic Load (Erlangs)

Ave

rage

PS

NR

PB

PSR

CS


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ProblemCan performance guarantees be provided to VBR video withoutsignificantly under-utilizing the bandwidth and can this be done inconjunction with minimizing the blocking probability for voice and datatraffic?

AlternativelyHow do we decide, how much of the available bandwidth should beallocated to each traffic class?

Solution Allocate bandwidth so the maximum call blocking probability is minimized


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Problem Statement

Given the aggregate load Di for each traffic type Ti and the total systembandwidth B, determine the allocated transmission capacity for each traffictype such that

Θ = ≥max ( ( ) )i

i iP d t B is minimized

B Bii

N

=∑ =

1subject to where d ti ( ) is the instantaneous demand


Theorem

Let X1 , …, XN be independent random variables taking theirvalues from the interval [0, 1]. Their probability distributions areotherwise arbitrary and not necessarily identical. Setand then for any the following estimation holds:

Furthermore, this estimation is best possible in the following sense:For any fixed and for any fixed D and C with there existinfinitely many counter examples for which the reverse holds.

X Xii=∑

D E X=

C D≥

( )P X CD

Ce

CC D≥ ≤

−

C D≥ε > 0

0LQLPL]HΘ = ≥max ( ( ) )

iP di t Bi


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New Problem Statement

Given the aggregate load Di for each traffic type Ti and the totalsystem transmission bandwidth B, determine the allocatedtransmission capacity for each traffic type, such that

is minimizedΘ =

−max

i

i

i

B

B DD

Be

i

i i

subject to B Bii

N

=∑ =

1

Θ = ≥max ( ( ) )i

P di t Bi


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Can prove: Allocation of Bi is asymptotically optimal if and only if

Thus the problem is solved by letting,

and iteratively searching for a value for whichholds within a given error bound

D

Be

D

Be

B

B D N

N

B

B DN

N N1

1

1

1 1

= =

− −K holds !

D

Bei

i

B

B Di

i i

=− σ

0 1< ≤σ B Bii

N

=∑ =

1ε > 0


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Method

If Xi is the size of ith frame (or region or subband) in a video sequencewhich has n frames, and if we define:

then by letting and deriving

we can calculate the mean and determine Di

where M is the # of video connections to be supported

( )Y X X Xn n= max , ,1 2 K

( )f yYnY Y

nn=

→ ∞lim

D Mi y= × η

ηy


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-0.010

0.010.020.030.040.050.060.070.080.09

1 11 21 31 41 51Size (Kbits)

Pro

bab

ility

Den

sity

Observed Normal

Lognormal Gamma

Weibull

Bi-Normal distribution“Amadeus”

-0.01

0

0.01

0.02

0.03

0.04

0.05

0.06

Size (Kbits)

Pro

bab

ility

Den

sity

Inter-Frame Coding“Headline News”

Gamma

Lognormal

Observed

Avg. PSNR = 30.44 dB

-0.01

0

0.01

0.02

0.03

0.04

0.05

0.06

1 16 31 46 61 76 91

Size (Kbits)

Pro

bab

ility

Den

sity

Intra-Frame Coding“Headline News”

Avg. PSNR = 31.52 dB

QQ plot for “Headline News" (JPEG)Using Weibull Distribution

0

10

20

30

40

50

60

0 10 20 30 40 50 60

Normal Quantile

Ob

serv

ed Q

uan

tile


6WDWLVWLFDO�&KDUDFWHUL]DWLRQ��9%5�9LGHR�Mean Square Error (inter-frame compressed, CNN sequence)

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

0 8 16 24 32 40 48

Time (Seconds)

Mo

del

ing

Err

or

(Mb

its)

Captured & Compressed @ 15 fps

-0.05

0

0.05

0.1

0.15

0.2

0.25

5 10 15 20 25 30 35 40 45 50

Image Frame Size (Kbits)

Pro

bab

ility

Den

sity

Captured & Compressed @ 5 fps

-0.01

0

0.01

0.02

0.03

0.04

0.05

0.06

0 5 10 15 20 25 30 35 40 45 50

Image Frame Size (Kbits)

Pro

bab

ility

Den

sity

Avg. Mean Square Error

Normal = 0.042793Gamma = 0.041567Lognormal = 0.037322Weibul = 0.070562

mean = 14.62par = 5.83cov = 0.57avg. dev.= 6.69

mean = 5.17par = 9.89cov = 1.17avg. dev.= 4.12


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Coding Request Video Conference Video Technique

High Capture Low Capture High Capture Low Capture Rate Rate Rate Rate

High Action Low Action High Action Low Action Low Action Low Action

Intra-Frame Normal Normal/Gamma Normal/Weibull Weibull Bi-Normal Bi-Normal

Inter-Frame Bi-Normal Gamma/Lognormal Normal Gamma Gamma-Pareto Lognormal

Results from distribution-based modeling of single VBR video sources

F y e yYy u( ) exp[ ],( )= − − ∞ < < ∞− −α

( ) ( )F x exg x= − −1 ( )g x is a increasing function of X

Notice, most video frame distributions can be described as:

where

then we can prove,


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l Thus knowing the mean can be computed as:

where and are the location and scale parameters

l For Voice connections:

where C is CBR (e.g. GSM - 13 KHz, DECT - 32 KHz, …) and M isthe estimated number of voice connections

Note: Can use Di s for Network Capacity Planning !

( )F yY

ηαy u= +

0 577.

u ( )α > 0

D Ma = × C

( )f yYn ( )Di yη


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Begin

End

l u= =0 1,

σ =+l u

2

( )~B Bii

N= =∑ σ1

~B B< − ε

~B B>

u = σ

( ) ( )B B BN

BN1 1

= =σ σ, ,K

l = σ

Midpoint of [ ]l u,

Initialize

Yes

No

No

Compute TotalCapacity

Check if Capacityis overused

Check if Capacityis underused

Compute ( )Bi

σ

Stricterbound

Ease upon bound


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Assume TDM-based system with 99 BBU

Let the aggregate average demands beD1 (voice) = 20 Erlang and D2 (video) = 40 Erlangs

Then the Load Proportional approach givesB1 = 33, B2 = 66, with Erlang’s blocking probability = 1 %

Smart Allocate algorithm givesB1 = 37 and B2 = 62, blocking probability = 0.57%

Improvement of 43% !


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l Accommodates all traffic classes without shutting out any single one

l Provides QoS guarantees for on-going VBR real-time videocommunications. This guarantee does not come at the expense ofbandwidth and other traffic classes.

l Robust and insensitive to statistical assumptions. Does not requiredetailed knowledge of traffic, only aggregate average values (detailedstatistical information is typically unavailable)

l Allocation based on minimizing a bound on the blocking probabilitiesthat is proven to be asymptotically optimal - significant as it signifies thatfor large number of systems it is sufficient to know aggregate flow rates.


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l Ad-Hoc multi-hop home networking– Protocols, Routing algorithms, architecture, roaming, locating, performance etc

l Hardware for hand-held communicators– RF issues, form factor, capacity, capability, user requirements, user interface etc.

l QoS in mobile multimedia– Content-Sensitive Video Coding (beyond MPEG-4), Resource assignment and

management, etc.

l Operating system support for mobile users– caching, hoarding, prediction, data management, disconnected operations etc

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