On Packetization of Embedded Multimedia Bitstreams

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 data Recyn Recyn data error

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 1 stream 2 stream 3 packet 1 packet 2 stream 3 stream4 stream 5 stream 6 ......... 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 packet 1 stream 2 packet 2 stream 3 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 • 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 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 Bk

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 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

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 Bk, 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