Video Frame Redundancy via Information Dispersal
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Solution Overview
Problem
Conventional data recovery techniques for video content transmission, such as high-quality-of-service circuits and packet retransmission, are not feasible due to cost or equipment limitations, and fail to account for the specific characteristics of encoded video data, leading to inefficiencies and visual artifacts from data loss or corruption.
Innovation Solution
A method and system that divide video data frames into segments using an information dispersal algorithm, distributing these segments across multiple frames to provide redundancy, allowing for reconstruction of corrupted frames during transmission, while limiting redundancy overhead to maintain efficient bandwidth usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional data recovery techniques (high-QoS circuits or packet retransmission) are used, then data reliability is improved, but device complexity and cost increase
Solution Approach 1:
The video data is divided into multiple segments using an information dispersal algorithm. Each segment contains distributed information from the original data, allowing reconstruction even if some segments are lost. This segmentation approach provides reliability without requiring complex retransmission protocols or high-QoS circuits.
Solution Approach 2:
Redundant data segments are generated and distributed across the transmission stream before any data loss occurs. This preliminary creation of redundancy allows the receiver to reconstruct corrupted frames without needing complex real-time error correction equipment or retransmission capabilities.
2Reliability
If redundant data segments are distributed across multiple frames, then data reliability is improved, but bandwidth efficiency deteriorates
Solution Approach 1:
Instead of providing complete redundancy for every possible data loss scenario, the system uses information dispersal to provide partial redundancy that is sufficient for practical reconstruction. The redundant segments are distributed sparsely across frames rather than duplicating entire frames, achieving acceptable reliability with reduced overhead.
Solution Approach 2:
The system changes the parameter of redundancy representation by using information dispersal algorithms that encode redundancy more efficiently than simple duplication. This transforms the redundancy from exact copies to distributed mathematical representations, reducing the bandwidth overhead while maintaining reconstruction capability.
3Productivity
If information dispersal algorithm is used to create redundant segments, then processing efficiency is improved, but manufacturing precision deteriorates
Solution Approach 1:
The system replaces mechanical copying methods with mathematical information dispersal algorithms. This substitution enables automated, high-speed processing of video data while maintaining precise reconstruction through mathematical relationships rather than physical duplication, improving processing efficiency without sacrificing accuracy.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, a method for providing redundancy in a video data stream. For every data frame of a plurality of data frames of video content, the method includes dividing the data frame into a plurality of data segments and copying a plurality of each of the plurality of data segments to the plurality of data frames in a data stream to provide redundancy for the data frame within the plurality of data frames in the data stream. The plurality of each of the plurality of data segments is distributed across the plurality of data frames to provide generate a plurality of redundancy-enhanced data frames that provide the redundancy over a protection interval associated with a network path. The method includes transmitting a data stream comprising the plurality of redundancy-enhanced data frames to a receiver via the network path. Other embodiments are disclosed.


