1. On Packetization of Embedded Multimedia Bitstreams Xiaolin Wu, Samuel Cheng, and Zixiang Xiong IEEE Transactions On Multimedia, March 2001

2. Outline Introduction packetization Problem formulation Optimal Packetization High Bit Rate Low Bit Rate Result Conclusion

3. Introduction Problem of multimedia communication packet dropping corrupted packets Techniques to alleviate or recover error detection codes automatic repeat request (ARQ) forward error correction (FEC) Error Concealment

4. Introduction (cont.) Resynchronization periodic symbols insert into the compressed source bit-streams. Confine errors to local segment of long message Error resilience dataRecyn error Recyndata

5. Packetization packet independent data partition bit-stream compression RLC - make bit-stream different size Question : How to pack variable length bit-streams into packets of a fixed size

6. Packetization (cont.) One of the solution is to fill the packets with the bit-streams sequentially. defeating the purpose of resynchronization stream 1stream 2stream 3 stream4stream 5 stream 6......... packet 1 packet 2 packet 3 Recynchronization marker

7. Packetization (cont.) Another solution is to enforce the alignment of the bitstream not 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

8. Problem Formulation Embedded bit-stream Given 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

9. Problem Formulation (cont.) K sample blocks S 1, S 2,......., S K compressed independently of each other Compressed bitstream B i, 1  i  K scalable in rate-distortion. N i Length of B i, 1  i  K M packet of payload L

10. Optimal Packetization(OP) We want to select ML bits to be packeted into M packets. To minimize the damage of packet loss by packet alignment constraints To minimize the distortion under packet alignment constraints

11. High Bit Rate Case one bitstreams have to occupy an integer number of packet pre-defined function: : the distortion of first a bits of B k

12. High Bit Rate Case(cont.) Original greedy approach sort all Δ value in descending order pick the M largest distortion reductions Question : Not contiguous subsequence from first bit of the embedded bitstream

13. High Bit Rate Case(cont.) Improved algorithm: Maintain a pointer p k for each bitstream Bk, 1  k  K. Initialize p k = 1, 1  k  K ; m=0; repeat find j such that Δ j (p j, L) = max 1  k  K Δ k (p k, L) pack this L bits into packet m; p k = p k + L; m = m + 1; until m = M; Add L bits of bitstream b k will reduce the most distortion

14. High Bit Rate Case(cont.) nonconvex operation R-D function solve D(M, K) in bottom-up Dynamic programming

15. Low Bit Rate Case We often have M < K allow more than one embedded bitstreams to be packed into one packet If k bitstreams are to share a packet, they have to be completely contained in that packet.

16. Low Bit Rate Case (cont.) NP complete If we impose an order for bitstreams B k, and allow a packet to contain only consecutive bitstreams in this order, this problem is solvable.

17. Low Bit Rate Case (cont.) minimum distortion of Bu,...,Bv Dynamic programming function

18. Result:

19. Result (cont.)

20. Conclusion Optimal Packetization is addressed under both low and high bit rate case Using dynamic programming for nonconvex distortion