Super Generation Network Coding for Content Distribution
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Solution Overview
Problem
Peer-to-peer content distribution systems face challenges such as computational complexity and reduced performance due to the high overhead of conventional network coding methods, which hinder efficient content distribution and availability.
Innovation Solution
The implementation of Super Generation Network Coding (SGNC) that partitions content into larger generations while maintaining low computational complexity, using three types of information pieces encoded with specific coefficients from Galois Fields, to enhance content availability and reduce overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Dense Network Coding is used to improve content distribution performance, then content availability and download speed are improved, but computational complexity and overhead increase significantly
Solution Approach 1:
The patent divides the content into multiple generations, where each generation consists of multiple information pieces. This segmentation allows the system to process and transmit smaller units of data at a time, reducing the computational burden on individual nodes while maintaining the benefits of network coding. The generator node creates multiple generations with different encoding patterns, enabling receivers to selectively decode based on their needs without processing the entire content at once.
Solution Approach 2:
The patent introduces different types of information pieces (first type with all unity coefficients, second type with some Galois Field coefficients, third type with all Galois Field coefficients) to provide local quality variations. This allows receivers to receive and process only the necessary portions of encoded data, optimizing the balance between content availability and computational requirements for each node in the network.
2Reliability
If conventional network coding schemes are used to enhance content distribution, then content availability improves, but computational overhead increases making it non-viable for real-world systems
Solution Approach 1:
The patent segments the content into generations and information pieces, allowing distributed processing. Each node only needs to process a subset of the data (one generation at a time) rather than the entire content, significantly reducing computational overhead while maintaining content availability through the structured generation approach.
Solution Approach 2:
The patent uses partial encoding by creating multiple generations with different encoding densities. Not all information pieces require full Galois Field encoding (third type), as some can use simpler unity coefficients (first type) or partial Galois Field coefficients (second type). This partial action approach reduces overall computational overhead while still providing sufficient content availability and redundancy.
3Device complexity
If larger generations are used to reduce the number of coding operations, then computational complexity decreases, but content distribution efficiency may be compromised
Solution Approach 1:
The patent applies different encoding qualities to different information pieces within the same generation. By having some pieces with unity coefficients and others with Galois Field coefficients, the system optimizes the trade-off between computational complexity and distribution efficiency. This local differentiation allows larger generations to be processed with reduced complexity while maintaining high distribution efficiency through the mixed encoding strategy.
Data Source
AI summary
A device, a method, and a non-transitory computer readable medium for distributing content to a plurality of nodes on a computer network are disclosed. The method includes dividing the content into a plurality of information groups, each of the information groups including a mutually exclusive subset of information pieces of the content, encoding the information pieces included in each of the plurality of information groups based on a combination of the information pieces included in the respective information group and a plurality of coefficients associated with the information pieces included in the respective information group, and distributing the encoded information pieces to the plurality of nodes on the computer network, wherein each of the information pieces is encoded into one of three types of information pieces.