On Packetization of Embedded Multimedia Bitstreams
Xiaolin Wu, Samuel Cheng, and Zixiang Xiong
IEEE Transactions On Multimedia, March 2001IEEE Transactions On Multimedia, March 2001
Outline
Introductionpacketization
Problem formulation Optimal Packetization
High Bit RateLow Bit Rate
Result Conclusion
Introduction
Problem of multimedia communicationpacket dropping corrupted packets
Techniques to alleviate or recovererror detection codesautomatic repeat request (ARQ)forward error correction (FEC)
• Error Concealment
Introduction (cont.)
Resynchronizationperiodic symbols insert into the
compressed source bit-streams.
Confine errors to local segment of long message
Error resilience
dataRecyn
error
Recyn data
Packetization
packet independentdata partition
bit-stream compressionRLC - make bit-stream different size
Question :
How to pack variable length bit-streams into packets of a fixed size
Packetization (cont.)
One of the solution is to fill the packets with the bit-streams sequentially.defeating the purpose of resynchronization
stream 1 stream 2 stream 3
stream 3 stream4 stream 5
stream 6 .........
packet 1
packet 2
packet 3
Recynchronization marker
Packetization (cont.)
Another solution is to enforce the alignment of the bitstreamnot allowing any bit-stream to start in the
middle of a packet.packetization inefficiency
stream 1
stream 2
stream 3
packet 1
packet 2
packet 3
Problem Formulation
Embedded bit-streamGiven a traversal, the resulting binary
sequence is a so-called embedded bitstream.• several pass such as bit-plane coding
Scalability in reconstruction quality.• can be truncated at any location
Problem Formulation (cont.)
K sample blocks S1, S2, ......., SK
compressed independently of each other Compressed bitstream Bi, 1 i K
• scalable in rate-distortion. Ni Length of Bi, 1 i K
M packet of payload LMLN
Kkk
1
Optimal Packetization(OP)
We want to select ML bits to be packeted into M packets.To minimize the damage of packet los
s by packet alignment constraintsTo minimize the distortion under pack
et alignment constraints
High Bit Rate Case
one bitstreams have to occupy an integer number of packet
pre-defined function: : the distortion of first a bits of Bk
)(adk
)()(),( ladadla kkk
High Bit Rate Case(cont.)
Original greedy approach
sort all Δ value in descending orderpick the M largest distortion reducti
ons
1/0,1),,( LNiKkLiL kk
Question :Not contiguous subsequence from first bit of the embedded bitstream
High Bit Rate Case(cont.)
Improved algorithm:
Maintain a pointer pk for each bitstream
Bk, 1 k K.
Initialize pk = 1, 1 k K ; m=0;
repeat
find j such that Δj (pj, L) = max 1 k K Δk(pk, L)
pack this L bits into packet m;
pk = pk + L;
m = m + 1;
until m = M;
Add L bits of bitstream bk will reduce the most distortion
High Bit Rate Case(cont.)
nonconvex operation R-D function
solve D(M, K) in bottom-up Dynamic programming
Low Bit Rate Case
We often have M < K allow more than one embedded bitstre
ams to be packed into one packet If k bitstreams are to share a packet, t
hey have to be completely contained in that packet.
Low Bit Rate Case (cont.)
NP complete
If we impose an order for bitstreams Bk, and allow a packet to contain only consecutive bitstreams in this order, this problem is solvable.
Low Bit Rate Case (cont.)
minimum distortion of Bu,...,Bv Dynamic programming function
),( vu
Result:
Result (cont.)
Conclusion
Optimal Packetization is addressed under both low and high bit rate case
Using dynamic programming for nonconvex distortion
