Growth Codes: Intermediate Performance Analysis and Application to Video

Growth codes are a subclass of Rateless codes that have found interesting applications in data dissemination problems. Compared to other Rateless and conventional channel codes, Growth codes show improved intermediate performance which is particularly useful in applications where partial data presents some utility. In this paper, we investigate the asymptotic performance of Growth codes using the Wormald method, which was proposed for studying the Peeling Decoder of LDPC and LDGM codes. Compared to previous works, the Wormald differential equations are set on nodes' perspective which enables a numerical solution to the computation of the expected asymptotic decoding performance of Growth codes. Our framework is appropriate for any class of Rateless codes that does not include a precoding step. We further study the performance of Growth codes with moderate and large size codeblocks through simulations and we use the generalized logistic function to model the decoding probability. We then exploit the decoding probability model in an illustrative application of Growth codes to error resilient video transmission. The video transmission problem is cast as a joint source and channel rate allocation problem that is shown to be convex with respect to the channel rate. This illustrative application permits to highlight the main advantage of Growth codes, namely improved performance in the intermediate loss region.

[1]  M. Fukamiya The Lipschitz Condition of Random Function , 1940 .

[2]  F. J. Richards A Flexible Growth Function for Empirical Use , 1959 .

[3]  Shu Lin,et al.  Error control coding : fundamentals and applications , 1983 .

[4]  Béla Bollobás,et al.  Random Graphs , 1985 .

[5]  Rajeev Motwani,et al.  Randomized Algorithms , 1995, SIGA.

[6]  N. Wormald Differential Equations for Random Processes and Random Graphs , 1995 .

[7]  Michael Luby,et al.  A digital fountain approach to reliable distribution of bulk data , 1998, SIGCOMM '98.

[8]  Pascal Frossard,et al.  Joint source/FEC rate selection for quality-optimal MPEG-2 video delivery , 2001, IEEE Trans. Image Process..

[9]  Béla Bollobás,et al.  Random Graphs: Notation , 2001 .

[10]  Michael Luby,et al.  LT codes , 2002, The 43rd Annual IEEE Symposium on Foundations of Computer Science, 2002. Proceedings..

[11]  A. Shokrollahi Raptor codes , 2004, International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings..

[12]  Jon Feldman,et al.  Growth codes: maximizing sensor network data persistence , 2006, SIGCOMM 2006.

[13]  Alexandros G. Dimakis,et al.  Unequal Growth Codes: Intermediate Performance and Unequal Error Protection for Video Streaming , 2007, 2007 IEEE 9th Workshop on Multimedia Signal Processing.

[14]  Sujay Sanghavi Intermediate Performance of Rateless Codes , 2007, 2007 IEEE Information Theory Workshop.

[15]  Nazanin Rahnavard,et al.  Rateless Codes With Unequal Error Protection Property , 2007, IEEE Transactions on Information Theory.

[16]  Rüdiger L. Urbanke,et al.  Modern Coding Theory , 2008 .

[17]  Muhammad Altaf,et al.  H.264 video streaming with data-partitioning and Growth codes , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[18]  Zixiang Xiong,et al.  Scalable Video Multicast Using Expanding Window Fountain Codes , 2009, IEEE Transactions on Multimedia.

[19]  Shenghao Yang,et al.  Coding for a network coded fountain , 2011, 2011 IEEE International Symposium on Information Theory Proceedings.

[20]  Nazanin Rahnavard,et al.  On the Intermediate Symbol Recovery Rate of Rateless Codes , 2012, IEEE Transactions on Communications.

[21]  Shenghao Yang,et al.  Batched Sparse Codes , 2012, IEEE Transactions on Information Theory.