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  3. Network-aware Source Coding and Communication
Publisher:
Cambridge University Press
Online publication date:
October 2011
Print publication year:
2011
Online ISBN:
9781139034357
DOI:
https://doi.org/10.1017/CBO9781139034357
Subjects:
Communications and Signal Processing, Engineering, Wireless Communications
Information

Book description

An introduction to the theory and techniques for achieving high quality network communication with the best possible bandwidth economy, this book focuses on network information flow with fidelity. Covering both lossless and lossy source reconstruction, it is illustrated throughout with real-world applications, including sensor networks and multimedia communications. Practical algorithms are presented, developing novel techniques for tackling design problems in joint network-source coding via collaborative multiple description coding, progressive coding, diversity routing and network coding. With systematic introductions to the basic theories of distributed source coding, network coding and multiple description coding, this is an ideal self-contained resource for researchers and students in information theory and network theory.

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Contents

Contents
References
References
[1] D., Slepian and J. K., Wolf. Noiseless coding of correlated information sources. IEEE Trans. on Inform. Theory, 19, 1973.
[2] L. H., Ozarow. On a source coding problem with two channels and three receivers. Bell System Technical Journal, 59(10), 1980.
[3] T., Ho, M., Medard, M., Effros, and R., Koetter. Network coding for correlated sources. CISS, 2004.
[4] A., Ramamoorthy, P. A., Chou, K., Jain, and M., Effros. Separating distributed source coding from network coding. Allerton Conference on Communication, Control and Computing, 2004.
[5] http://www.microsoft.com/tv/iptvedition.mspx
[6] http://www.planet.nl/planet/show/id=118880/contentid=668582/sc=780960
[7] http://www.shoutcast.com/ttsl.html
[8] R., Ahlswede, N., Cai, S. Y. R., Li, and R. W., Yeung. Network information flow. IEEE Trans. on Inform. Theory, 46, 2000.
[9] S. Y. R., Li, R.W., Yeung, and N., Cai. Linear network coding. IEEE Trans. on Inform. Theory, 49, 2003.
[10] A. R., Lehman and E., Lehman. Complexity classification of network information flow problems. Allerton Communication, and Computing, 2003.
[11] S., Jaggi, P., Sanders, P. A., Chou, M., Effros, S., Egner, K., Jain, and L., Tolhuizen. Polynomial time algorithms for multicast network code construction. IEEE Trans. on Inform. Theory, 51, 2005.
[12] P., Sanders, S., Egner, and L., Tolhuizen. Polynomial time algorithms for network information flow. ACM SPAA, 2003.
[13] K., Jain, M., Mahdian, and M. R., Salavatipour. Packing steiner trees. ACM SODA, 2003.
[14] S., Hougardy and H. J., Prömel. A 1.598 approximation algorithm for the steiner problem in graphs. In ACM SODA, 1999.
[15] G., Robins and A., Zelikovsky. Improved Steiner tree approximation in graphs. Proceedings of SODA, 770–779, 2000.
[16] L. C., Lau. An approximate max-steiner-tree-packing min-steiner-cut theorem. In IEEE FOCS, 2004.
[17] Nicholas J. A., Harvey, Robert, Kleinberg, and April, Rasala Lehman. Comparing network coding with multicommodity flow for the k-pairs communication problem. CSAIL Technical Reports, 2004.
[18] Nicholas J. A., Harvey, Robert, Kleinberg, and April, Rasala Lehman. On the capacity of information networks. IEEE/ACM Trans. Netw., 14:2345–2364, 2006.
[19] T., Leighton and S., Rao. Multicommodity max-flow min-cut theorems and their use in designing approximation algorithms. Journal of the ACM, 46(6):787–832, 1999.
[20] Z., Li and B., Li. Network coding in undirected networks. Proc. of CISS, 2004.
[21] B., Li and Z., Li. Network coding: the case of multiple unicast sessions. Proceedings of the 42nd Allerton Annual Conference on Communication, Control, and Computing, 2004.
[22] K., Jain, V. V., Vazirani, and G., Yuval. On the capacity of multiple unicast sessions in undirected graphs. IEEE/ACM Transactions on Networking, 14:2805–2809, 2006.
[23] Z., Reznic, R., Zamir, and M., Feder. Joint source-channel coding of a gaussian mixture source over a gaussian broadcast channel. In IEEE Trans. on Inform. Theory, 48, 2002.
[24] A., Albanese, J., Blomer, J., Edmonds, M., Sudan, and M., Luby. Priority encoding transmission. IEEE Trans. on Inform. Theory, 42, 1996.
[25] R., Venkataramani, G., Kramer, and V. K., Goyal. Multiple description coding with many channels. IEEE Trans. on Inform. Theory, 49, 2003.
[26] A. D., Taubman and M., Marcellin. Jpeg2000: Image compression fundamentals, practice and standard. Berlin: Kluwer Academic Publishers, 2001.
[27] W., Li. Overview of fine granularity scalability in mpeg-4 video standard. IEEE Trans. Circuits Syst. Video Techn., 11, 2001.
[28] X., Wu, B., Ma, and N., Sarshar. Rainbow network problems and multiple description coding. IEEE ISIT, 2005.
[29] L., Lastras and T., Berger. All sources are nearly successively refinable. IEEE Trans. Inform. Theory, 47, 2001.
[30] A., Said and W., Pearlman. A new, fast and efficient image codec based on set partitioning in hierarchical trees. IEEE Trans. on Circuits and Syst. for Video Tech., 6:243–250, 1996.
[31] T., Burger. Rate Distortion Theory: A Mathematical Basis for Data Compression. Englewood Cliffs: Prentice-Hall, 1974.
[32] Taekon, Kim, Hyun, Mun Kim, Ping-Sing, Tsai, and T., Acharya. Memory efficient progressive rate-distortion algorithm for jpeg 2000. IEEE Trans. on Circuits and Systems for Video Tech., 15, 2005.
[33] S., Boyd and L., Vandenberghe. Convex Optimization. Online: http://www.stanford.edu/boyd/cvxbook/
[34] S., Dumitrescu, X., Wu, and Z., Wang. Globally optimal uneven error protected packetization of scalable code streams. IEEE Trans. on Multimedia, 6, 2004.
[35] R., Puri and K., Ramchandran. Multiple description source coding through forward error correction codes. In Proc. 33rd Asilomar Conference on Signals, Systems, and Computers, volume 1, pp. 342–346, 1999.
[36] A. E., Mohr, R. E., Ladner, and E. A., Riskin. Approximately optimal assignment for unequal loss protection. IEEE ICIP, 2000.
[37] A. E., Mohr, E. A., Riskin, and R. E., Ladner. Graceful degradation over packet erasure channels through forward error correction. IEEE DCC, 1999.
[38] T., Stockhammer and C., Buchner. Progressive texture video streaming for lossy packet networks. Int. Packet Video Workshop, 2001.
[39] V., Stankovic, R., Hamzaoui, and Z., Xiong. Packet loss protection of embedded data with fast local search. IEEE ICIP, 2002.
[40] R., Albert and A. L., Barabasi. Statistical mechanics of complex networks. Reviews of Modern Physics, 74, 47 (2002).
[41] http://www.ilog.com/products/cplex
[42] M. R., Garey and D. S., Johnson. Computers and intractability: A guide to the theory of np-completeness. San Francisco: W. H. Freeman and Company, 1979.
[43] A. V., Goldberg and R. E., Tarjan. A new approach to the maximum-flow problem. Journal of the ACM, 35(4):921–940, 1988.
[44] V. A., Vaishampayan. Design of multiple-description scalar quantizers. IEEE Trans. Inform. Theory, 39(3):821–834, 1993.
[45] D., Muresan and M., Effros. Quantization as histogram segmentation: globally optimal scalar quantizer design in network systems. In Proc. Data Compression Conf., pp. 302–311, 2002.
[46] H., Jeffreys and B. S., Jeffreys. Dirichlet integrals. In Methods of Mathematical Physics, 3d edn. Cambridge: Cambridge University Press, pp. 468–470, 1988.
[47] V. K., Goyal. Multiple description coding: compression meets the network. IEEE Signal Process. Mag., 18(5):74–93, September 2001.
[48] P., Sanders, S., Egner, P., Chou, M., Effros, S., Egner, K., Jain, and L., Tolhuizen. Polynomial time algorithms for multicast network code construction. IEEE Trans. Inf. Theory, 51, 2005.
[49] N., Sundaram, P., Ramanathan, and S., Banerjee. Multirate media stream using network coding. Proceedings of Allerton 05, 2005.
[50] X., Wu, M., Shao, and N., Sarshar. Rainbow network flow with network coding. Proceedings of NetCod 2008, January 2008.
[51] L., Chen, T., Ho, S. H., Low, M., Chiang, and J. C., Doyle. Optimization based rate control for multicast with network coding. Proceedings of INFOCOM 2007, May 2007, pp. 1163–1171.
[52] R. W., Yeung. Multilevel diversity coding with distortion. IEEE Trans. Inf. Theory, 41(2):412–422, March 1995.
[53] Y., Wu. http://ip.hhi.de/imagecom_g1/savce/downloads/svc-reference-software.htm
[54] H., Schwarz, D., Marpe, and T., Wiegand. Overview of the scalable video coding extension of the h.264/avc standard. IEEE Transactions on Circuits and Systems for Video Technology, Special Issue on Scalable Video Coding, 17(9):1103–1120, 2007.
[55] D., Schonberg, K., Ramchandran, and S. S., Pradhan. Distributed code constructions for the entire Slepian-Wolf rate region for arbitrarily correlated sources. IEEE DCC, 2004, pp. 292–301.
[56] Y., Matsunaga and H., Yamamoto. A coding theorem for lossy data compression by LDPC codes. IEEE Trans. Inform. Theory, 49, 2225–2229, 2003.
[57] R., Yeung, S.-Y., Li, and N., Cai. Network Coding Theory (Foundations and Trends in Communications and Information Theory). Delft, the Netherlands: Now Publishers, 2006.
[58] T., Ho and D., Lun. Network Coding: An Introduction. Cambridge: Cambridge University Press, 2008.
[59] B., Rimoldi and R., Urbanke. Asynchronous Slepian-Wolf coding via source-splitting. IEEE Int. Symp. Inf. Theory, p. 271, 1997.
[60] T. M., Cover and J. A., Thomas. Elements of Information Theory. New York: Wiley, 1991.
[61] A., Grand, B., Rimoldi, R., Urbanke, and P. A., Whiting. Rate-splitting multiple access for discrete memoryless channels. IEEE Trans. on Inform. Theory, 47, no. 3, 873–890, 2001.
[62] Y., Cassuto and J., Bruck. Network coding for non-uniform demands. IEEE ISIT, 2005.
[63] T. P., Coleman, A. H., Lee, M., Medard, and M., Effros. A new source-splitting approach to the Slepian-Wolf problem. IEEE ISIT, p. 332, 2004.
[64] T. P., Coleman, A. H., Lee, M., Medard, and M., Effros. Low-complexity approaches to Slepian-Wolf near-lossless distributed data compression. IEEE Trans. Inform. Theory, 52, 3546–3561, 2006.
[65] S. S., Pradhan, K., Ramchandran, and R., Koetter. A constructive approach to distributed source coding with symmetric rates. IEEE Int. Symp. Inf. Theory, 178, 2000.
[66] V., Stankovic, A., Liveris, Z., Xiong, and C., Georghiades. Design of Slepian-Wolf codes by channel code partitioning. Proc. Data Comp. Conf., Snowbird, UT, 2004, pp. 302–311.
[67] Christina, Fragouli and Emina, Soljanin. Information flow decomposition for network coding. IEEE Trans. on Inform. Theory, 52(3): 829–848, 2006.
[68] M., Sartipi and F., Fekri. Distributed source coding in wireless sensor networks using LDPC coding: the entire Slepian-Wolf rate region. IEEE Wireless Communications and Networking Conference, pp. 1939–1944, Mar. 2005.
[69] A. D., Wyner. Recent results in the Shannon theory. IEEE Trans. Inform. Theory, IT-20, pp. 2–10, Jan. 1974.
[70] A., Aggarwal, M., Klave, S., Moran, P., Shor, and R., Wilber. Geometric applications of a matrix-searching algorithm. Algorithmica, 2, pp. 195–208, 1987.
[71] A., Aggarwal, B., Schieber, and T., Tokuyama. Finding a minimum-weight k-link path in graphs with the concave monge property and applications. Discrete & Computational Geometry, 12, pp. 263–280, 1994.
[72] A., Albanese, J., Blomer, J., Edmonds, M., Luby, and M., Sudan. Priority encoding transmission. IEEE Trans. Inform. Theory, 42, pp. 1737–1744, Nov. 1996.
[73] A., Apostolico and Z., Galil. (eds.). Pattern Matching Algorithms, New York: Oxford University Press, 1997.
[74] T. Y., Berger-Wolf and E. M., Reingold. Index assignment for multichannel communication under failure. IEEE Trans. Inform. Theory, 48(10):2656–2668, Oct. 2002.
[75] S. N., Diggavi, N. J. A., Sloane, and V. A., Vaishampayan. Asymmetric multiple description lattice vector quantizers. IEEE Trans. Inform. Theory, 48(1):174–191, Jan. 2002.
[76] S., Dumitrescu, X., Wu, and Z., Wang. Globally optimal uneven error-protected packetization of scalable code streams. IEEE Trans. Multimedia, 6(2):230–239, Apr. 2004.
[77] S., Dumitrescu and X., Wu. Optimal two-description scalar quantizer design. Algorithmica, 41(4):300, 269–287, Feb. 2005.
[78] S., Dumitrescu and X., Wu. On global optimality of gradient descent algorithms for fixed-rate scalar multiple description quantizer design. Proc. IEEE DCC'05, pp. 388–397, March 2005.
[79] S., Dumitrescu, X., Wu, and Z., Wang. Efficient algorithms for optimal uneven protection of single and multiple scalable code streams against packet erasures. IEEE Trans. Multimedia, 9(7):1466–1474, Nov. 2007.
[80] S., Dumitrescu. Speed-up of encoder optimization step in multiple description scalar quantizer design. Proc. of IEEE Data Compression Conference, pp. 382–391, March 2008, Snowbird, UT.
[81] S., Dumitrescu and X., Wu. Lagrangian optimization of two-description scalar quantizers. IEEE Trans. Inform. Theory, 53(11):3990–4012, Nov. 2008.
[82] S., Dumitrescu and X., Wu. On properties of locally optimal multiple description scalar quantizers with convex cells, to appear in IEEE Trans. on Inform. Theory.
[83] M., Effros and L., Schulman. Rapid near-optimal VQ design with a deterministic data net. Proc. ISIT'04, pp. 298, July 2004.
[84] M., Effros. Optimal multiple description and multiresolution scalar quantizer design. Information Theory and Applications Workshop'08, Univesity of California, San Diego, 27 January–1 February 2008.
[85] M., Fleming, Q., Zhao, and M., Effros. Network vector quantization. IEEE Trans. Inform. Theory, 50(8), Aug. 2004.
[86] M., Garey, D. S., Johnson, and H. S., Witsenhausen. The complexity of the generalized lloydcmax problem. IEEE Trans. Inform. Theory, 28(2):255–266, Mar. 1982.
[87] V. K., Goyal, J. A., Kelner, and J., Kovačević. Multiple description vector quantization with a coarse lattice. IEEE Trans. Inform. Theory, 48, pp. 781–788, Mar. 2002.
[88] S. P., Lloyd. Least squares quantization in PCM. IEEE Trans. Inform. Theory, IT-28, pp. 129–137, Mar. 1982.
[89] A. E., Mohr, E. A., Riskin, and R. E., Ladner. Unequal loss protection: graceful degradation over packet erasure channels through forward error correction. IEEE Journal on Selected Areas in Communication, 18(7):819–828, Jun. 2000.
[90] D., Muresan and M., Effros. Quantization as histogram segmentation: optimal scalar quantizer design in network systems. IEEE Trans. Inform. Theory, 54(1):344–366, Jan. 2008.
[91] J., Østergaard, J., Jensen, and R., Heusdens. n-channel entropy-constrained multiple-description lattice vector quantization. IEEE Trans. Inform. Theory, 52(5):1956–1973, May 2006.
[92] R., Puri and K., Ramchandran. Multiple description source coding through forward error correction codes. Proc. 33rd Asilomar Conference on Signals, Systems, and Computers, vol. 1, California, Oct. 1999, pp. 342–346.
[93] D. G., Sachs, R., Anand, and K., Ramchandran. Wireless image transmission using multiple-description based concatenated codes. Proc. SPIE 2000, vol. 3974, pp. 300–311, Jan. 2000.
[94] S. D., Servetto, V. A., Vaishampayan, and N. J. A., Sloane. Multiple description lattice vector quantization. IEEE Proc. Data Compression Conf., Mar. 1999, pp. 13–22.
[95] V., Stankovic, R., Hamzaoui, and Z., Xiong. Efficient channel code rate selection algorithms for forward error correction of packetized multimedia bitstreams in varying channels. IEEE Trans. Multimedia, 14(2):240–248, Apr. 2004.
[96] C., Tian, S., Mohajer, and S., Diggavi. On the symmetric Gaussian multiple description rate-distortion function. IEEE DCC, Snowbird, UT, March 2008, pp. 402–411.
[97] C., Tian and S., Hemami. Sequential design of multiple description scalar quantizers. IEEE Data Compression Conference, pp. 32–42, 2004.
[98] V. A., Vaishampayan and J., Domaszewicz. Design of entropy-constrained multiple-description scalar quantizers. IEEE Trans. Inform. Theory, 40(1):245–250, Jan. 1994.
[99] V. A., Vaishampayan, N. J. A., Sloane, and S. D., Servetto. Multiple description vector quantization with lattice codebooks: Design and analysis. IEEE Trans. Inform. Theory, 47(5):1718–1734, July 2001.
[100] J., Barros and S. D., Servetto. Network information flow with correlated sources. IEEE Trans. Inform. Theory, 52, pp. 155–170, Jan. 2006.
[101] T. S., Han and K., Kobayshi. A unified achievable region for a general class of multiterminal source coding systems. IEEE Trans. Inform. Theory, 26, pp. 277–288, May 1980.
[102] M., Effros, M., Mdard, T., Ho, S., Ray, D., Karger, and R., Koetter. Linear network codes: A unified framework for source, channel and network coding. Proc. DIMACS Workshop Networking Information Theory, Piscataway, NJ, 2003.
[103] T., Ho, M., Medard, R., Koetter, D. R., Karger, M., Effros, J., Shi, and B., Leong. A random linear network coding approach to multicast. IEEE Trans. Inform. Theory, 53, pp. 4413–4430, Oct. 2006.
[104] A., Ramamoorthy, K., Jain, P. A., Chou, and M., Effros. Separating distributed source coding from network coding. IEEE Trans. Inform. Theory, 52, pp. 2785–2795, June 2006.
[105] P., Tan, K., Xie, and J., Li. Slepian-Wolf coding using parity approach and syndrome approach. Proc. CISS, pp. 708–713, March 2007.
[106] T., Berger, Z., Zhang, and H., Viswanathan. The CEO problem. IEEE Trans. Inform. Theory, 42, pp. 887–902, May 1996.
[107] T., Berger and R., Yeung. Multiterminal source coding with encoder breakdown. IEEE Trans. Inform. Theory, 35, pp. 237–244, Mar. 1989.
[108] J., Chen and T., Berger. Robust distributed source coding. IEEE Trans. Inform. Theory, 54, pp. 3385–3398, Aug. 2008.
[109] I., Csiszar. The method of types. IEEE Trans. Inform. Theory, 44, pp. 2505–2523, Oct. 1998.
[110] I., Csiszar and J., Korner. Information Theory: Coding Theorems for Discrete Memoryless Systems. New York: Academic, 1981.
[111] R. L., Dobrushin and B. S., Tsybakov. Information transmission with additional noise. IRE Trans. Inform. Theory, 18, pp. 293–304, 1962.
[112] A., Kaspi and T., Berger. Rate-distortion for correlated sources with partially separated encoders. IEEE Trans. Inf. Theory, 28, pp. 828–840, Nov. 1982.
[113] Z., Liu, S., Cheng, A. D., Liveris, and Z., Xiong. Slepian-Wolf coded nested lattice quantization for Wyner-Ziv coding: high-rate performance analysis and code design. IEEE Trans. Inform. Theory, 52, pp. 4358–4369, Oct. 2006.
[114] Y., Oohama. Gaussian multiterminal source coding. IEEE Trans. Inform. Theory, 43, pp. 1912–1923, Nov. 1997.
[115] Y., Oohama. The rate-distortion function for the Quadratic Gaussian CEO problem. IEEE Trans. Inform. Theory, 44, pp. 1057–1070, May 1998.
[116] Y., Wang, M. T., Orchard, and A. R., Reibman. Multiple description image coding for noisy channels by pairing transform coefficients. Proc. IEEE Workshop on Multimedia Signal Processing, Princeton, NJ, pp. 419–424, June 1997.
[117] V. K., Goyal and J., Kovačević. Optimal multiple description transform coding of Gaussian vectors. Proc. IEEE Data Compression Conf., Snowbird, UT, pp. 388–397, Mar.–April 1998.
[118] V. K., Goyal and J., Kovačević. Generalized multiple descriptions coding with correlating transforms. IEEE Trans. Inform. Theory, 47(6):2199–2224, Sep. 2001.
[119] V. K., Goyal, J., Kovačević, and M., Vetterli. Multiple description transform coding: robustness to erasures using tight frame expansions. Proc. IEEE Int. Symp. Inform. Theory, pp. 408, Aug. 1998.
[120] V. K., Goyal, J., Kovačević, and M., Vetterli. Quantized frame expansions as source-channel codes for erasure channels. Proc. IEEE Data Compression, pp. 326–335, Mar. 1999.
[121] P. A., Chou, S., Mehrota, and A., Wang. Multiple description decoding of overcomplete expansions using projections onto convex sets. Proc. IEEE DCCn, pp. 72–81, Mar. 1999.
[122] V. K., Goyal, M., Vetterli, and N. T., Thao. Quantized overcomplete expansions in RN. Proc. IEEE Trans. Inform. Theory, 44, pp. 16–31, Jan. 1998.
[123] S., Rangan and V., Goyal. Recursive consistent estimation with bounded noise. Proc. IEEE Trans. Inform. Theory, 47, pp. 457–464, Jan. 2001.
[124] D., Taubman. High performance scalable image compression with EBCOT. IEEE Trans. Image Processing, 9, pp. 1158–1170, July 2000.
[125] N., Sarshar and X., Wu. Rate-distortion optimized multimedia communication in networks. Proceedings of SPIE 08, volume 6822, January 2008.
[126] X., Wu, B., Ma, and N., Sarshar. Rainbow network flow of multiple description codes. IEEE Trans. Inf. Theory, 54(10):4565–4574, Oct. 2008.
[127] S.-Y.R., Li, R.W., Yeung, and N., Cai. Linear Network Coding. IEEE Trans. Inf. Theory, 49(2):371–381, Feb. 2003.
[128] N., Gortz and P., Leelapornchai. Optimization of the index assignments for multiple description vector quantizers. IEEE Trans. Communications, 51(3):336–340, Mar. 2003.
[129] J. H., Conway and N. J. A., Sloane. Sphere Packings, Lattices, and Groups. Berlin: Springer, 1998.
[130] J., Hopcroft and R., Karp. An o(n5/2) algorithm for maximum matchings in bipartite graphs. SIAM Journal on Computing, 2(4):225–231, 1973.
[131] J., Østergaard, J., Jensen, and R., Heusdens. n-channel symmetric multiple-description lattice vector quantization. Proc. IEEE Data Compression Conf., Mar. 2005, pp. 378–387.
[132] X., Huang and X., Wu. Optimal index assignment for multiple description lattice vector quantization. Proc. of DCC 2006.
[133] S., Tavildar, P., Viswanath, and A. B., Wagner. The Gaussian many-help-one distributed source coding problem. IEEE ITW, pp. 596–600, 2006.
[134] H., Viswanathan and T., Berger. The quadratic Gaussian CEO problem. IEEE Trans. Inform. Theory, 43, pp. 1549–1559, Sept. 1997.
[135] A. B., Wanger, S., Tavildar, and P., Viswanath. Rate region of the quadratic Gaussian two-encoder source-coding problem. IEEE Trans. Inf. Theory, 54, pp. 1938–1961, May, 2008.
[136] H. S., Witsenhausen. Indirect rate distortion problems. IEEE Trans. Inform. Theory, 26, pp. 518–521, Sept. 1980.
[137] A. D., Wyner and J., Ziv. The rate-distortion function for source coding with side information at the decoder. IEEE Trans. Inform. Theory, IT-22, pp. 1–10, Jan. 1976.
[138] M., Shao, S., Dumitrescu, and X., Wu. Toward the optimal multirate multicast for lossy packet network. Proceedings of ACM Multimedia, 27–31 October 2008.
[139] R., Zamir, S., Shamai, and U., Erez. Nested linear/lattice codes for structured multiterminal binning. IEEE Trans. Inform. Theory, 48, pp. 1250–1276, Jun. 2002.
[140] R., Cristescu, B., Beferull-Lozano, and M., Vetterli. Networked Slepian-Wolf: theory, algorithms and scaling laws. IEEE Trans. Inform. Theory, 51, pp. 4057–4073, Dec. 2005.
[141] S., Dumitrescu and T., Zheng. Improved multiple description framework based on successively refinable quantization and uneven erasure protection. Proc. of IEEE Data Compression Conference, pp. 514–514, March 2008, Snowbird, UT.
[142] S., Dumitrescu, G., Rivers, and S., Shirani. Unequal erasure protection technique for scalable multi-streams. IEEE Transactions on Image Processing, 19(2):422–434, Feb. 2010.
[143] S., Dumitrescu, M., Shao, and X., Wu. Layered multicast with inter-layer network coding. Proceedings of the IEEE INFOCOM, pp. 442–449, 2009.

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