Density selection method based on tunable sparse network coding

Abstract

The invention belongs to the technical field of network coding, and specifically relates to a density selection method based on adjustable sparse network coding. The method includes sending a data stream by a source node according to the density value of the current sparse network coding, and finding that the current transmission data packet is a regenerative code The lower bound of the probability of the packet; the maximum budget value and the expected budget value required for the successful transmission from the source node to the sink node are calculated according to the lower bound of the probability of the regenerated encoded packet; the source node constructs the expected budget value according to the actual budget value that the channel can provide in the current stage The comparison inequality between the budget value and the actual budget value; adjust the actual density value under different density distribution functions, and select the best density at the current stage through the current receiving situation of the sink node, until the decoding matrix full-rank transmission is completed or the remaining actual The budget value is 0; the present invention selects the best density value suitable for the current stage through the budget comparison inequality, and solves the problems of limited bandwidth and insufficient budget caused by unstable link packet loss rate.

Description

Density selection method based on tunable sparse network coding technical field The present invention relates to the adjustable sparse network coding technical field under real-time multimedia network, be specifically related to a kind of based on Density selection method for tunable sparse network coding. Background technique The introduction of network coding (Network Coding, NC) technology has changed the transmission mode of traditional communication network, allowing Allows intermediate nodes to encode incoming information, rather than being limited to "store-forward" only, improving the efficiency of transmission and network The throughput of the network is higher, but the computational complexity of the NC is higher. In order to reduce the computational complexity, Danilo et al. proposed sparse network coding (Sparse Network Coding, SNC), i.e. random linear network coding only in overlapping subsets (Random Linear Network Coding, RLNC) method. Since SNC on...

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Problems solved by technology

The present invention adjusts the density according to the unified density distribution function for the existing research of TSNC, and does not consider the factors such as bandwidth limitation and unstable link state, which lead to the difficulty in selecting the optimal density, and the cost problem needs to be compromised, and the matrix rank probability model is used for analysis. The expected budget value of each generation of transmission and referring to the budget value in practical applications, combined with the linear density distribution function in TSNC and the classic staged density distribution function for transmission simulation, in order to obtain the optimal density applicable to the current stage under the constraints , so as to reduce the transmission delay by increasing the generation probability of regenerated encoded packets

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