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On Packetization of Embedded Multimedia Bitstreams. Xiaolin Wu, Samuel Cheng, and Zixiang Xiong. IEEE Transactions On Multimedia, March 2001. Outline. Introduction packetization Problem formulation Optimal Packetization High Bit Rate Low Bit Rate Result Conclusion. Introduction.

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## On Packetization of Embedded Multimedia Bitstreams

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**On Packetization of Embedded Multimedia Bitstreams**Xiaolin Wu, Samuel Cheng, and Zixiang Xiong IEEE Transactions On Multimedia, March 2001**Outline**• Introduction • packetization • Problem formulation • Optimal Packetization • High Bit Rate • Low Bit Rate • Result • Conclusion**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**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**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**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**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**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**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**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**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**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**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 bitstreams to be packed into one packet • If k bitstreams are to share a packet, they 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**Conclusion**• Optimal Packetization is addressed under both low and high bit rate case • Using dynamic programming for nonconvex distortion

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