Progressive Source Transmissions using
Joint Source-Channel Coding (JSCC)
and Hierarchical Modulation in
Packetized Networks
Suayb S. Arslan,
Pamela C. Cosman and Laurence B. Milstein
Department of Electrical and Computer Engineering
University of California, San Diego
12/03/2009
Hilton Hawaiian Village, Honolulu, Hawaii, USA
Preliminary Exam
1
Outline
 Progressive Sources
 UEP techniques
 A new UEP technique based on packetization
 System Model & Optimization
 Numerical Results
 Conclusion & Future work
Exam
IEEEPreliminary
GLOBECOM
2009
2



Progressive Source Compression


Progressive Sources
UEP techniques
System Model &
Optimization
Numerical Results
Conclusion & Future work
 Ex: SPIHT image compression algorithm [SPIHT ‘96].
4% gives you only a brief description of the source.
Exam
IEEEPreliminary
GLOBECOM
2009
3



Progressive Source Compression


Progressive Sources
UEP techniques
System Model &
Optimization
Numerical Results
Conclusion
 20% is good enough to say what the picture looks like.
Exam
IEEEPreliminary
GLOBECOM
2009
4



Progressive Source Compression


Progressive Sources
UEP techniques
System Model &
Optimization
Numerical Results
Conclusion
 At 40%, it begins to refine the image.
Exam
IEEEPreliminary
GLOBECOM
2009
5



Progressive Source Compression


Progressive Sources
UEP techniques
System Model &
Optimization
Numerical Results
Conclusion
 At 100%, it gives more refinement but no major
difference from 40%.
Exam
IEEEPreliminary
GLOBECOM
2009
6



Progressive Source Compression


Progressive Sources
UEP techniques
System Model &
Optimization
Numerical Results
Conclusion
 We consider progressive type of encoders.
(Embedded image encoders: EZW, SPIHT, etc…)
 Result: Very sensitive to bit errors.
 Protection and performance improvement is
achieved by channel coding.
 UEP: Unequal error protection is beneficial
for progressively encoded sources. This can
be provided by several known techniques.
Exam
IEEEPreliminary
GLOBECOM
2009
7





Error Propagation
Progressive Sources
UEP techniques
System Model &
Optimization
Numerical Results
Conclusion
 If we do not truncate, we end up with error propagation:
Single bit is
in error
0.25bpp=65536bits.
1500th bit position
Exam
IEEEPreliminary
GLOBECOM
2009
8
03/05/2009





Error Propagation
Progressive Sources
UEP techniques
System Model &
Optimization
Numerical Results
Conclusion
 If we do not truncate, we end up with error propagation:
Single bit is
in error
0.25bpp=65536bits.
1500th bit position 20000th bit position
Exam
IEEEPreliminary
GLOBECOM
2009
9





Error Propagation
Progressive Sources
UEP techniques
System Model &
Optimization
Numerical Results
Conclusion
 If we do not truncate, we end up with error propagation:
Single bit is
in error
0.25bpp=65536bits.
No error
0.075bpp=19660bits 20000th bit position
Exam
IEEEPreliminary
GLOBECOM
2009
10
Unequal Error Protection:
(a) Joint Source Channel Coding





Progressive Sources
UEP techniques
System Model &
Optimization
Numerical Results
Conclusion
 Different channel codes per packet. Each packet has
different significance in terms of the end
reconstruction quality of the source.
 For a given packet size, amount of information bits
and parity bits are subject to a trade off.
 Optimal allocation is studied in literature [Lu ‘98].
Exam
IEEEPreliminary
GLOBECOM
2009
11



Unequal Error Protection:
(b) Hierarchical Modulation






Progressive Sources
UEP techniques
System Model &
Optimization
Numerical Results
Conclusion
HP: High Priority
LP: Low Priority
is hierarchical
modulation
parameter.
HP and LP bits
have different
BERs even if
  3.

 It is used in DVB-T standard and many other hot spots.
 It is previously considered in an image transmission scenario
[Morimoto ‘96].
Exam
IEEEPreliminary
GLOBECOM
2009
12



Unequal Error Protection:
(c) Packetization & Channel Coding



Two different
packetization
strategies: Sequential
Packetization (SP) and
Folded Packetization
(FP).

BL bits are HP bits and
are more important
than EL bits, LP bits.

Since HP and LP BERs
are different, by using
FP we give more
protection to the first
half of the packets
than the second half.
Exam
IEEEPreliminary
GLOBECOM
2009
Progressive Sources
UEP techniques
System Model &
Optimization
Numerical Results
Conclusion
SP
FP
13



Progressive Sources
UEP techniques
System Model &
Optimization
Numerical Results
Conclusion
Unequal Error Protection:
(c) Packetization & Channel Coding - 2


 We obtain the packets of bits after CRC and channel
coding. We combine them using SP and FP, modulate and
produce packets of symbols as shown in a) and b).
Exam
IEEEPreliminary
GLOBECOM
2009
14



Progressive Sources
UEP techniques
System Model &
Optimization
Numerical Results
Conclusion
Unequal Error Protection:
(c) Packetization & Channel Coding - 3


Exam
IEEEPreliminary
GLOBECOM
2009
15



System Model - 1
Exam
IEEEPreliminary
GLOBECOM
2009


Progressive Sources
UEP techniques
System Model &
Optimization
Numerical Results
Conclusion
16



System Model - 2


Progressive Sources
UEP techniques
System Model &
Optimization
Numerical Results
Conclusion
 Define the following vectors:
 Where dl is the distortion up to and including packet l.
And r1 is the code rate protecting the first half, and r2
is the code rate protecting the second half.
 For a given r , we can determine d . It is used in our
optimization algorithm to find optimal hierarchical
parameters:
 Then we optimize over all ( r1, r2) pairs.
Exam
IEEEPreliminary
GLOBECOM
2009
17
Upper bounds for coded
Hierarchical System








Progressive Sources
UEP techniques
System Model &
Optimization
Numerical Results
Conclusion
We need to derive
BER/PER expressions for
coded hierarchical system.
We show that Viterbi hard
decision upper bounds for
BSC can be extended to
any hierarchical system
with L priority layers.
Upperbounds are not
tight, thus we use
approximate but more
accurate BER/PER curves.
They are obtained by
using nonlinear regression
methods.
Preliminary Exam
18



Optimization - 1


Progressive Sources
UEP techniques
System Model &
Optimization
Numerical Results
Conclusion
 We adopt distortion optimal approach. Average distortion function
is given by

i
is SNR, Pc is the probability that i-th packet is correctly
received. N: Total number of packets.
 Bounded constrained optimization problem is:

Exam
IEEEPreliminary
GLOBECOM
2009
19



Optimization - 2


Progressive Sources
UEP techniques
System Model &
Optimization
Numerical Results
Conclusion
 It can be converted into an optimization problem with inequality
constraints:

ui , li are upper and lower bounds for  i .
Exam
IEEEPreliminary
GLOBECOM
2009
20



Optimization - 3


Progressive Sources
UEP techniques
System Model &
Optimization
Numerical Results
Conclusion
 Lagrangian function is given by:

are Lagrangian multipliers.
 Unconstrained minimization problem is
 In trying to solve this problem, we need to solve a set of non
linear equations:
 We use numerical techniques.
Exam
IEEEPreliminary
GLOBECOM
2009
21



Optimization - 4


Progressive Sources
UEP techniques
System Model &
Optimization
Numerical Results
Conclusion
 We proved the following proposition for the
convexity of the cost function:
 We also proved and justified the following theorem:
Exam
IEEEPreliminary
GLOBECOM
2009
22



Numerical Results –


Simulation Parameters
Modulation: H-4PAM.
RCPC Code using Generator polynomial ¼ mother code : [117 127
155 171] in octal notation. Chosen from [Hagg ‘97], memory 6.








Progressive Sources
UEP techniques
System Model &
Optimization
Numerical Results
Conclusion

Cr  8 / 9,8 /10,8 /12,8 /14,8 /16,8 /18,8 / 20,8 / 22,8 / 24,8 / 26,8 / 28,8 / 30,8 / 32
A (Lx X Ly) Lena image SPIHT encoded w/o Arithmetic Coding.
Decoder: Hard decision Viterbi Algorithm.
Channel: AWGN and flat fading Rayleigh channel.
~
Packet size,   450.
Number of packets for each transmission rate in bpp (0.25bpp):
(l )
 bs : number of information bits for packet l
Exam
IEEEPreliminary
GLOBECOM
2009
23



Numerical Results -
Systems


Progressive Sources
UEP techniques
System Model &
Optimization
Numerical Results
Conclusion
 In an image transmission system (SPIHT), we have
the following systems:
 seqConv1: Sequential Packetization with optimal Code rate
using conventional 4PAM. (EEP scheme)
 foldConv1: Folded Packetization with optimal Code rate using
conventional 4PAM.
 foldHier1: Folded Packetization with optimal Code rate using
hierarchical 4PAM.
 foldHier2: Folded Packetization with two optimal Code rates,
using hierarchical 4PAM.
 Note that foldConv1, foldHier1, foldHier2 are UEP
schemes.
Exam
IEEEPreliminary
GLOBECOM
2009
24



Numerical Results
Exam
IEEEPreliminary
GLOBECOM
2009


Progressive Sources
UEP techniques
System Model &
Optimization
Numerical Results
Conclusion
25



Numerical Results
Exam
IEEEPreliminary
GLOBECOM
2009


Progressive Sources
UEP techniques
System Model &
Optimization
Numerical Results
Conclusion
26



Numerical Results


Progressive Sources
UEP techniques
System Model &
Optimization
Numerical Results
Conclusion
 UEP schemes perform better than EEP schemes.
 The non-concave behavior of these curves are a consequence that
the code rate set is discrete.
 The discrete nature of the code set is also the cause of non
uniform gains going from one UEP scheme to another.
 At low SNRs, the gap between the curves becomes more
pronounceable as UEP makes more sense when the channel
degrades.
 Hierarchical parameter determines the BER for each packet in
the first and second half of the packet stream simultaneously.
 foldHier2 is introduced to alleviate the constraints of
hierarchical parameter and code rate set.
Exam
IEEEPreliminary
GLOBECOM
2009
27



Numerical Results
Exam
IEEEPreliminary
GLOBECOM
2009


Progressive Sources
UEP techniques
System Model &
Optimization
Numerical Results
Conclusion
28



Numerical Results -
Explanations


Progressive Sources
UEP techniques
System Model &
Optimization
Numerical Results
Conclusion
 For foldHier2, it is observed that channel code rates
have the following relation: r1  r2
 In JSCC-only mechanism we would have r2  r1 . In
other words more protection for the first half of the
packet stream.
 Our conjecture is that it is because with hierarchical
modulation, higher code rate for the first half of the
packets means that we increase the number of
information bits in the first half. Then, the
hierarchical parameters adjust themselves to
protect the bits of the first half more than the bits
of the remaining half.
Exam
IEEEPreliminary
GLOBECOM
2009
29



Numerical Results
Exam
IEEEPreliminary
GLOBECOM
2009


Progressive Sources
UEP techniques
System Model &
Optimization
Numerical Results
Conclusion
30



Numerical Results -
Explanations


Progressive Sources
UEP techniques
System Model &
Optimization
Numerical Results
Conclusion
 propConv1 provides only two levels of protection.
 propHier1 and propHier2 provides as many protection levels as
the number of packets.
 propHier2 system is also compared with code rates in reverse
order r1  r2 , with optimal hierarchical parameter set. It is
observed that propHier2 protects almost all the packets better
than it does with the reverse order.
 This shows that UEP is provided with JSCC and Hierarchical
parameters. Since channel code allocation mechanism also
arranges the number information bits within each packet, our
system benefits from these two mechanisms (JSCC and
Hierarchical Modulation) in different ways.
Exam
IEEEPreliminary
GLOBECOM
2009
31





Conclusion


Our system provides UEP using three different mechanisms:



Packetization.
Channel Coding.
Hierarchical Modulation.

Hierarchical parameters determines PERs of the first and second half
of the packet stream simultaneously.
Code rate set is discrete and not necessarily capacity achieving.
We have the following constraints:





Progressive Sources
UEP techniques
System Model &
Optimization
Numerical Results
Conclusion
Joint optimization of these three mechanisms is performed.
C1: A new UEP method using packetization combined with
channel coding is proposed.
C2: Hierarchical JSCC system is shown to perform better than a
baseline JSCC-only mechanism.
C3: JSCC and Hierarchical parameters provide UEP in reverse
directions of the packet stream. In our system, JSCC allocates
more information bits in the first half of the packets.
Exam
IEEEPreliminary
GLOBECOM
2009
32



Final Remarks


Progressive Sources
UEP techniques
System Model &
Optimization
Numerical Results
Conclusion
 This work was supported by Intel Inc., the
Center for Wireless Communications (CWC) at
UCSD, and the UC Discovery Grant program of
the state of California.
Exam
IEEEPreliminary
GLOBECOM
2009
33





References








Progressive Sources
UEP techniques
System Model &
Optimization
Numerical Results
Conclusion
[SPIHT ‘96], Said A. and Pearlman W.A., “A New Fast and Efficient Image Codec Based on
Set Partitioning in Hierarchical Trees,” IEEE Transactions on Circuits and Systems for Video
Technology, vol. 6, pp. 243-250, June 1996.
[Morimoto ‘96], Morimoto, M., Okada, M., and Komaki, S., “A Hierarchical System for
Miltimedia Mobile Communication,” First International Workshop on Wireless Image/Video
Communications, Sep. 1996.
[Lu ‘98] J. Lu, A. Nosratinia, and B. Aazhang, “Progressive source channel coding of images
over bursty error channels,” in Proc. International Conference on Image Processing, Chicago,
Ill, USA, October 1998.
Kleider J.E. and Abousleman G.P., Robust Image Transmission using Source Adaptive
Modulation and Trellis-Coded Quantization, Image Processing, 1999. ICIP 99. Proceedings.,
Vol.1 pp.396 - 400 1999.
Pei, Y. Modestino, J.W. , “Multi-layered video transmission over wireless channels using an
adaptive modulation and coding scheme”, Proceedings of IEEE International Conference on
Image Processing ,vol. 2, pp. 10091012, Thessaloniki, Greece, October 2001.
P.G. Sherwood and K. Zeger, “Progressive Image Coding for Noisy Channels,” IEEE Signal
Processing Letters, vol. 4, No. 7, pp. 189-191, July. 1997
[Hagg ‘97] Hagenauer, J. “Rate-Compatible Punctured Convolutional Codes(RCPC Codes)
and Their Applications,” IEEE Transactions on Communications, vol. 36, No. 4, pp. 389-400,
April. 1997.
J. G. Proakis, Digital Communications, 3rd edition, New York: Mc-Graw Hill, 1995.
Exam
IEEEPreliminary
GLOBECOM
2009
34