Approximate decoding for network coded inter-dependent data

In this paper, we consider decoding of loss tolerant data encoded by network coding and transmitted over error-prone networks. Intermediate network nodes typically perform the random linear network coding in a Galois field and a Gaussian elimination is used for decoding process in the terminal nodes. In such settings, conventional decoding approaches can unfortunately not reconstruct any encoded data unless they receive at least as many coded packets as the original number of packets. In this paper, we rather propose to exploit the incomplete data at a receiver without major modifications to the conventional decoding architecture. We study the problem of approximate decoding for inter-dependent sources where the difference between source vectors is characterized by a unimodal distribution. We propose a mode-based algorithm for approximate decoding, where the mode of the source data distribution is used to reconstruct source data. We further improve the mode-based approximate decoding algorithm by using additional short information that is referred to as position similarity information (PSI). We analytically study the impact of PSI size on the approximate decoding performance and show that the optimal size of PSI can be determined based on performance requirements of applications. The proposed approach has been tested in an illustrative example of data collection in sensor networks. The simulation results confirm the benefits of approximate decoding for inter-dependent sources and further show that 93.3% of decoding errors are eliminated when the approximate decoding uses appropriate PSI

Network coding of correlated data with approximate decoding

We consider the problem of distributed delivery of correlated data from sensors in ad hoc network topologies. We propose to use network coding in order to exploit the path diversity in the network for efficient delivery of the sensor information. We further show that the correlation between the data sources can be exploited at receivers for efficient approximate decoding when the number of received data packets is not sufficient for perfect decoding. We analyze how the decoding performance is influenced by the choice of the network coding parameters and in particular by the size of finite fields. We determine the optimal field size that maximizes the expected decoding performance, which actually represents a trade-off between information loss incurred by quantizing the source data and the error probability in the reconstructed data. Moreover, we show that the decoding performance improves when the accuracy of the correlation estimation increases. We have illustrated our network coding based algorithms with approximate decoding in sensor networks and video coding applications. In both cases, the experimental results confirm the validity of our analysis and demonstrate the benefits of our solution for distributed delivery of correlated information in ad hoc networks

Approximate Decoding Approaches for Network Coded Correlated Data

This paper considers a framework where data from correlated sources are transmitted with help of network coding in ad-hoc network topologies. The correlated data are encoded independently at sensors and network coding is employed in the intermediate nodes in order to improve the data delivery performance. In such settings, we focus on the problem of reconstructing the sources at decoder when perfect decoding is not possible due to losses or bandwidth bottlenecks. We first show that the source data similarity can be used at decoder to permit decoding based on a novel and simple approximate decoding scheme. We analyze the influence of the network coding parameters and in particular the size of finite coding fields on the decoding performance. We further determine the optimal field size that maximizes the expected decoding performance as a trade-off between information loss incurred by limiting the resolution of the source data and the error probability in the reconstructed data. Moreover, we show that the performance of the approximate decoding improves when the accuracy of the source model increases even with simple approximate decoding techniques. We provide illustrative examples about the possible of our algorithms that can be deployed in sensor networks and distributed imaging applications. In both cases, the experimental results confirm the validity of our analysis and demonstrate the benefits of our low complexity solution for delivery of correlated data sources

Coalition based Multimedia Peer Matching Strategies for P2P Networks

In this paper, we consider the problem of matching users for multimedia transmission in peer-to-peer (P2P) networks and identify strategies for fair resource division among the matched multimedia peers. We propose a framework for coalition formation, which enables users to form a group of matched peers where they can interact cooperatively and negotiate resources based on their satisfaction with the coalition, determined by explicitly considering the peer’s multimedia attributes. In addition, our proposed approach goes a step further by introducing the concept of marginal contribution, which is the value improvement of the coalition induced by an incoming peer. We show that the best way for a peer to select a coalition is to choose the coalition that provides the largest division of marginal contribution given a deployed value-division scheme. Moreover, we model the utility function by explicitly considering each peer’s attributes as well as the cost for uploading content. To quantify the benefit that users derive from a coalition, we define the value of a coalition based on the total utility that all peers can achieve jointly in the coalition. Based on this definition of the coalition value, we use an axiomatic bargaining solution in order to fairly negotiate the value division of the upload bandwidth given each peer’s attributes

TRANSMISSION OF CORRELATED INFORMATION SOURCES WITH NETWORK CODING

This paper addresses the problem of the distributed delivery of correlated data sources with help of network coding. Network coding provides an alternative to routing algorithms and offers improved system performance, robustness and throughput, with no need of deploying sophisticated routing strategies. However, the performance is directly driven by the number of innovative data packets that reach the receiver. If the number of received innovative data packets is significantly small, the decoder cannot perfectly recover the transmitted information. However, we show that the correlation between the data sources can be used at decoder for effective approximate decoding. We analytically investigate the impact of the network coding algorithm, and in particular, of the size of finite fields on the decoding performance. Then, we determine an optimal field size that minimizes the expected decoding error, which represents a trade-off between quantization of the source data and probability of decoding error. The network coding with approximate decoding algorithm is implemented in illustrative multimedia streaming and sensor network applications. In both cases, the experimental results confirm the field size analysis and illustrate the effectiveness of approximate decoding of correlated data. 1