A digital fountain approach to reliable distribution of bulk data
Proceedings of the ACM SIGCOMM '98 conference on Applications, technologies, architectures, and protocols for computer communication
Analysis of random processes via And-Or tree evaluation
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
FOCS '02 Proceedings of the 43rd Symposium on Foundations of Computer Science
IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
Near Optimal Update-Broadcast of Data Sets
MDM '07 Proceedings of the 2007 International Conference on Mobile Data Management
Modern Coding Theory
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Analysis of sum-product decoding of low-density parity-check codes using a Gaussian approximation
IEEE Transactions on Information Theory
Raptor codes on binary memoryless symmetric channels
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Distributed Joint Source-Channel Coding of Video Using Raptor Codes
IEEE Journal on Selected Areas in Communications
Wyner–Ziv Video Compression and Fountain Codes for Receiver-Driven Layered Multicast
IEEE Transactions on Circuits and Systems for Video Technology
Rateless distributed source code design
Proceedings of the 5th International ICST Mobile Multimedia Communications Conference
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Fountain codes are a robust solution for data multicasting to a large number of receivers which experience variable channel conditions and different packet loss rates. However, the standard fountain code design becomes inefficient if all receivers have access to some side information correlated with the source information. We focus our attention on the cases where the correlation of the source and side information can be modelled by a binary erasure channel (BEC) or by a binary input additive white Gaussian noise channel (BIAWGNC). We analyse the performance of fountain codes in data multicasting with side information for these cases, derive bounds on their performance and provide a fast and robust linear programming optimization framework for code parameters. We demonstrate that systematic Raptor code design can be employed as a possible solution to the problem at the cost of higher encoding/decoding complexity, as it reduces the side information scenario to a channel coding problem. However, our results also indicate that a simpler solution, non-systematic LT and Raptor codes, can be designed to perform close to the information theoretic bounds.