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Video Communication over Wired
and Wireless CDMA Networkswith Optimal Bandwidth Allocation
Yushi Shen
Advisors: Prof. Pamela C. Cosman
Prof. Laurence B. Milstein
2005 June 20th University Qualifying Exam 1
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Introduction• Area: Video communications
• Channel model:Tandem wired and wireless CDMA networks– Wired component: Packet erasures
– Wireless component: Burst errors• Direct-sequence (DS) CDMA technology
Three components:
– Source Coding– Channel Coding– Spread Spectrum
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Introduction
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Introduction• Problem:
Bandwidth allocation among source coding, channelcoding and spreading, with a target frame rate, under thetotal bandwidth (chip rate) constraint:
R s: source bit rate (bit/s); r
c: channel coding rate;
M : spreading gain; W : total bandwidth (chip rate, chip/s).
• Adaptive allocation: – Changing channel characteristics
– Different source content
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Talk Outline Introduction
2. Computation of Packet Error Rate3. Tradeoff between Channel Coding and
Spreading
4. Source Encoding with Optimal Mode
Selection
5. Optimal Bandwidth Allocation6. Simulation Results
7. Future Work
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Computation of Packet Error RatePacket Error Rate (P):
• The key factor for the performance
• Determined by:
– Packet erasure rate introduced by wired link: Pe
– Packet drop rate due to uncorrectable bit errors by
wireless link: P p
• Total packet error rate:
P = Pe + P p - Pe x P p
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Computation of Packet Error RatePacket Drop Rate due to Wireless Link (P p):
– y : fixed packet length
– Ad : weight distribution of block codes
– : received SNIR (signal-to-noise-plus-interference-ratio)
r γ
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Computation of Packet Error RateComputation of Weight Distribution ( Ad ):
• Computed based on the transition matrix sequence
• Computed
– For each RCPC code (characterized by rate r c)
– Given fixed packet length ( y)
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Computation of Packet Error Rate
Computation of received SNIR ( ):
=
– : channel gain;
K : # of users; L: # of resolvable paths;
E b: energy per source bit; N 0: energy of Gaussian noise;
: exponential multipath intensity profile
– Result for sufficiently large number of users
• Multi-access-interference is asymptotically Gaussian
• Self-interference is negligible
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(forward)
α
λ
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Computation of Packet Error RatePacket Drop Rate due to Wireless Link (P p):
– Weight distribution ( Ad ), which is determined by
• Structure of RCPC code (characterized by r c)
• Fixed packet length ( y)
– Received SNIR ( ), which is determined by
• System parameters (K , L, E b, N 0)
• Current channel conditions ( )
• Channel coding rate (r c ) and spreading gain ( M )
α
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Computation of Packet Error RateConclusion: Given system parameters, and conditioned on
channel characteristics, packet error rate is fully determinedby channel coding rate (r c ) and spreading gain ( M ). (forward)
X axis: Received SNIR
(dB)
Y axis: Probability of packet error
Rate 1/3 RCPC code
Rate 2/3 RCPC codeRate 8/9 RCPC code
No FEC used
2005 June 20th University Qualifying Exam 11
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Talk Outline Introduction
Computation of Packet Error Rate
3. Tradeoff between Channel Coding and
Spreading
4. Source Encoding with Optimal Mode
Selection
5. Optimal Bandwidth Allocation6. Simulation Results
7. Future Work
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Tradeoff between Channel Coding and Spreading,
under a Target Packet Error Rate
• r c and M the packet error rate, given the system
parameters and conditioned on the channelcharacteristics.
• r c and M the source bit rate ( Rs), under the
bandwidth constraint.
• Basic idea:
– Choose the (r c , M ) for each packet that maximizes Rs, among all the (r c , M ) combinations that achievethe target packet error rate.
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Tradeoff between Channel Coding and Spreading,
under a Target Packet Error Rate
Algorithm:
• Pre-calculate the relationship between packet error andreceived SNIR, and solve the SNIR threshold under the
target packet error rate, for each RCPC code. (example)
• During transmission
– Estimate the current channel characteristics;
– For each r c , calculate the smallest M that achieves the
SNIR threshold for the target, and get a (r c , M ) pair;
– Among all the (r c , M ) pairs, choose the one that
minimizes the ( M / r c) ratio to maximize Rs.
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Tradeoff between Channel Coding and Spreading,
under a Target Packet Error Rate
It can be shown the encoder will actually select the
same r c , and only vary M , most of the time.• For each r c , denote the SNIR threshold for thetarget packet error rate
• Solve M in the SNIR equation and plug in ( M / r c): (see)
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• To minimize ( M/ r c ) is equivalent to maximize
• RCPC code with lowest rate has the highest ratio
≈cr
M
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Tradeoff between Channel Coding and Spreading,
under a Target Packet Error Rate
Conclusion:
• The tradeoff between channel coding andspreading:
– Achieve the target packet error rate
– Maximize the source bit rate
– Satisfy the bandwidth constraint
• Under the scenario of larger number of users(self-interference becomes negligible), FEC ismore important than spreading
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Talk Outline Introduction
Computation of Packet Error Rate
Tradeoff between Channel Coding and
Spreading
4. Source Encoding with Optimal Mode
Selection
5. Optimal Bandwidth Allocation6. Simulation Results
7. Future Work
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Source Encoding: Intra-Coding Mode
CurrentFrame
PreviousFrame
INTRA Coding
Macro
Block
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Source Encoding: Inter vs. Intra Coding
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• Inter-Mode coding:
– High compression efficiency – Propagates past errors
– More suitable for good channel conditions
• Intra-Mode coding: – Stops error propagation
– Costly in bits in general
– More suitable for bad channel conditions
• It is desired to switch between two modes according
to channel conditions and video content
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Fixed-length Packet• Fixed-length packet
• Re-sync per packet• A MB is reconstructable at the decoder if:
– All packets containing this MB are received
• Illustration:
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Rate-Distortion Optimization• Every MB is tentatively encoded using both
intra/inter mode and 31 quantization steps• For each possibility, distortion ( D MB) and rate
usage ( R MB), thus the chip used for the MB (W MB),
are calculated
• To minimize D MB under the constrained of frame
rate target. This is given by the Lagrangian
2005 June 20th University Qualifying Exam 23
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Source Encoding: Algorithm Summary• Computation of expected distortion at the encoder
is recursive on a per-pixel basis
• Uses the estimated distortion within a rate-
distortion framework for optimal mode-switching
• Optimal mode selection algorithm
– Given the packet error rate – Given the target frame rate
2005 June 20th University Qualifying Exam 24
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Talk Outline Introduction
Computation of Packet Error Rate
Tradeoff between Channel Coding and
Spreading
Source Encoding with Optimal ModeSelection
5. Optimal Bandwidth Allocation6. Simulation Results
7. Future Work
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Optimal Bandwidth Allocation• Problem:
Allocation bandwidth among Rs, r c and M , tooptimize the overall performance, under:
– Bandwidth constraint
– Frame rate target (transmission time)
• Effects:
– Changing channel characteristics
– Different source content
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Optimal Bandwidth AllocationAlgorithm:
• Predetermine a set of target packet error rates
• For each packet: 2-step tradeoff (1) For each target packet error rate
Trade off channel coding and spreading, to maximize
the source bit rate ( Rs) and achieve the target packet errorrate (P p).
If no (r c, M ) pair can achieve a certain P p, skip this P p.
If none of the packet error rates can be achieved, reportdeep fading and temporarily send nothing (except pilotbits for channel estimation).
Result: a set of (P p , Rs , r c , M ) 4-tuples
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Optimal Bandwidth AllocationAlgorithm (cont.):
• For each packet: 2-step tradeoff
(2) Trade off source coding rate and packet error rate
For each (P p , Rs , r c , M ) 4-tuple, using the video
encoder encodes the current content ( MB by MB),
until the encoded bits fill the fixed length packet.
Choose the tuple that optimizes the performance:
minimizing the expected distortion per time unit.
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Optimal Bandwidth AllocationAlgorithm (cont.):
• Illustration of a typical packetization after source encoding
• Select the 4-tuple that minimizes the distortion per time
unit, which is approximately determined by
2005 June 20th University Qualifying Exam 29
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Optimal Bandwidth Allocation• Computation complexity:
Let N 1 denote the number of candidate packet error rate, N 2 the number of MBs per packet, the encoder needs to
tentatively encode ( N 1x N 2 ) MBs for each packet
• Choices of P p candidates:
– Too large (P p >5%): error rate goes up quickly to unity
– Too small (P p
<0.1%): under the same Rs
, performance
gain diminishes dramatically
– Our Choices of P p : [0.2%, 0.6%, 1%, 1.5%, 3%]
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Optimal Bandwidth AllocationSummary:
Optimal bandwidth allocation among source coding,channel coding and spreading for video
communications over tandem channels
– Adaptively at the packet level
– Incorporates the effect of changing channel
conditions– Incorporates the effect of current video content
2005 June 20th University Qualifying Exam 31
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Talk Outline Introduction
Computation of Packet Error Rate
Tradeoff between Channel Coding and
Spreading
Source Encoding with Optimal ModeSelection
Optimal Bandwidth Allocation
6. Simulation Results
7. Future Work
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Simulation Result: PSNR vs. Chip RatePSNR performance versus target transmission chip rate,
Carphone QCIF, and E b /N 0 =4dB.
X axis: Chip Rate W
(Mcps)
Y axis: PSNR (dB)Optimal bandwidth
allocation
Allocation with fixed
target P p=1%
Allocation with
P p=1% and a fixed M =15
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Simulation Result: PSNR vs. Eb /N0PSNR performance versus Eb /N0, Carphone QCIF, chip rate
15Mcps.
X axis: Eb /N0 (dB)
Y axis: PSNR (dB)
Optimal bandwidth
allocation
Allocation with fixed
target P p=1%
Allocation with
P p=1% and a fixed M =15
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Simulation Result: Packet Error Rate UsagePercentage of the total packets that employ the corresponding
target drop rate as optimal bandwidth allocation, chip rate 15
Mcps, and E b /N 0 =4dB.
X axis: Target Packet
Error Rate Pp (%)Y axis: Percentage (%)
Carphone QCIF
(high motion)
Mother and
daughter QCIF (low
motion)
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Talk Outline Introduction
Computation of Packet Error Rate
Tradeoff between Channel Coding and
Spreading
Source Encoding with Optimal ModeSelection
Optimal Bandwidth Allocation
Simulation Results
7. Future Work
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Future Work • Channel Estimation, and System Sensitivity to
Estimation Error• Video Communications over Multi-Carrier (MC)
CDMA
– Spreading data stream in the freq domain over multiple
subcarriers
– Subcarriers convey the same information– Better freq diversity to combat selective fading
comparing to signal carrier
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Acknowledgements
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Appendix: Weight Distribution• Punctured Convolutional Codes:
– Mother convolutional code
– Puncturing table
• Rate-Compatible Punctured Convolutional (RCPC) Codes:
– The higher rate code is embedded in the lower rate codes
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Appendix: Weight Distribution (Cont.)• Transition Matrix of Convolutional Codes (C. C.)
Example:
• Transition Matrix Sequence of Punctured C. C.Example: for puncture table a(1):
where
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Appendix: Weight Distribution (Cont.)• Product Matrix
Φ=– y is the fixed packet length
• Weight Distribution for Block Codes
– Coming from the corresponding elements of Φ
– Example: Zero-tail method, T(x) is given byΦ1,1
∏=
y
ii A
1
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Appendix: Distortion per Time UnitDenominator: transmission
time of the packet (exactly)
Numerator: distortion of the
packet (approximate due to
the tails)– DCT transform
– Negligible, because tails are
much smaller than y
s
c
R
y
W
M r
y
=
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Appendix: System with Fixed Spreading GainSpreading gain ( M ) is fixed for all packets
Trade off between source coding and channel codingAlgorithm:
• Pre-calculate the relationship between packet error and
received SNIR, and solve the SNIR threshold under the
target packet error rate, for each RCPC code
• Under a target packet error rate (say 1%), use the RCPC
code with highest coding rate (thus maximize Rs) among
those achieve the target error rate.
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