Joint Source-Channel Decoding: A Cross-Layer Perspective by Pierre Duhamel, Michel Kieffer

By Pierre Duhamel, Michel Kieffer

  • Treats joint resource and channel deciphering in an built-in way
  • Gives a transparent description of the issues within the box including the mathematical instruments for his or her solution
  • Contains many precise examples precious for functional purposes of the speculation to video broadcasting over cellular and instant networks

Traditionally, cross-layer and joint source-channel coding have been noticeable as incompatible with classically established networks yet contemporary advances in thought replaced this example. Joint source-channel deciphering is now visible as a conceivable replacement to split deciphering of resource and channel codes, if the protocol layers are taken into consideration. A joint source/protocol/channel procedure is therefore addressed during this publication: all degrees of the protocol stack are thought of, exhibiting how the knowledge in every one layer impacts the others.

This e-book offers the instruments to teach how cross-layer and joint source-channel coding and deciphering are actually appropriate with present-day cellular and instant networks, with a specific program to the most important sector of video transmission to mobiles. ordinary functions are broadcasting, or point-to-point supply of multimedia contents, that are very well timed within the context of the present improvement of cellular prone equivalent to audio (MPEG4 AAC) or video (H263, H264) transmission utilizing fresh instant transmission criteria (DVH-H, DVB-SH, WiMAX, LTE).

This cross-disciplinary publication is perfect for graduate scholars, researchers, and extra ordinarily pros operating both in sign processing for communications or in networking purposes, drawn to trustworthy multimedia transmission. This publication can also be of curiosity to humans desirous about cross-layer optimization of cellular networks. Its content material could provide them with different issues of view on their optimization challenge, enlarging the set of instruments which they can use.

Pierre Duhamel is director of analysis at CNRS/ LSS and has formerly held examine positions at Thomson-CSF, CNET, and ENST, the place he was once head of the sign and snapshot Processing division. He has served as chairman of the DSP committee and affiliate Editor of the IEEE Transactions on sign Processing and sign Processing Letters, in addition to performing as a co-chair at MMSP and ICASSP meetings. He used to be offered the Grand Prix France Telecom through the French technology Academy in 2000. he's co-author of greater than eighty papers in foreign journals, 250 convention complaints, and 28 patents.

Michel Kieffer is an assistant professor in sign processing for communications on the Universit? Paris-Sud and a researcher on the Laboratoire des Signaux et Syst?mes, Gif-sur-Yvette, France. His study pursuits are in joint source-channel coding and interpreting concepts for the trustworthy transmission of multimedia contents. He serves as affiliate editor of sign Processing (Elsevier). he's co-author of greater than ninety contributions to journals, convention court cases, and ebook chapters.

  • Treats joint resource and channel deciphering in an built-in way
  • Gives a transparent description of the issues within the box including the mathematical instruments for his or her solution
  • Contains many special examples worthwhile for useful functions of the speculation to video broadcasting over cellular and instant networks

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Extra info for Joint Source-Channel Decoding: A Cross-Layer Perspective with Applications in Video Broadcasting

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1. Directed graph or automaton describing the transition of the Markov source of Nevertheless, one may transform a Markov model of order m in a Markov model (of order 1) by considering the random vector Xκ = (Xκ , . . , Xκ−m+1 ) taking values in Am . 6), one may easily deduce p Xκ = ajκ |Xκ−1 = ajκ−1 , Xκ−2 = ajκ−2 . . 6 ) for any ajκ−1 ∈ Am and ajκ ∈ Am . The state of the Markov model is now a vector of dimension m belonging to Am . Since this transformation is always possible, in the remainder of this book, only memoryless and Markov models of order 1 are considered.

In such codes, f does not belong to the state but is a variable resulting from state transitions (output variable). Thus, f is not considered in the computation of the coder states, and the state can take only a finite number of values, which characterizes an FSM. 2 gives the states, outputs, and all possible transitions of a quasi-arithmetic coder precomputed for a binary source with 2p = 4. The value of the variable f is not considered in this state model. Only the action of incrementing f when a rescaling takes place is signaled by the letter f in the table, also referred to as follow-up in a study by Howard and Vitter (1992).

3125)10 . To perform the decoding, one notices that c ∈ [1, 5[ /5, thus, x1 = 1; c ∈ [5, 9[ / 25, thus x2 = 0 etc. This arithmetic coder is not suited for practical implementations since it requires the representation of κ and hκ with a number of bits that is usually not compatible with the accuracy of standard processors. Finite-precision versions of the arithmetic coder have been proposed to solve this problem. , 1987). This integer interval may be viewed as a representation on p-bits of the interval [ 0 , h0 [ = [0, 1[ of the IP-AC.

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