Streaming Code Frame Splitting for Partial Burst Loss Recovery
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing videoconferencing systems face challenges in recovering from partial burst losses due to inefficiencies in FEC codes, particularly in real-time communications, where standard FEC codes like Reed-Solomon codes are inadequate for bursty packet losses, and streaming codes often fail to match real-world patterns.
Innovation Solution
A frame splitting encoder system that splits data frames into components, allocates parity symbols, and uses machine learning for adaptive frame splitting and parity allocation, combined with FEC-aware compression to enhance packet loss recovery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard FEC codes like Reed-Solomon codes are used for packet loss recovery, then the system provides error correction capability, but it is inefficient at recovering bursty packet losses in real-time communications
Solution Approach 1:
The patent segments data frames into multiple components and uses different FEC coding strategies for different segments. This allows targeted recovery of burst losses by treating each segment independently with appropriate coding parameters, improving both reliability and real-time performance compared to uniform Reed-Solomon coding across all packets.
Solution Approach 2:
The system dynamically adapts FEC coding parameters based on observed loss patterns. When burst losses are detected, the system adjusts coding rates and segmentations in real-time to match the actual channel conditions, making the error correction more efficient for the specific burst patterns encountered rather than using fixed Reed-Solomon parameters.
2Reliability
If retransmission-based approaches are used for packet loss recovery, then data can be recovered accurately, but the packet loss recovery delay time exceeds the short playback time requirement of live communications
Solution Approach 1:
The patent pre-encodes redundancy information into data frames before transmission using FEC codes. This preliminary action ensures that recovery data is already available at the receiver end, eliminating the need for time-consuming retransmission requests and responses. The recovery can proceed immediately using the pre-prepared parity information.
Solution Approach 2:
The system introduces FEC parity packets as an intermediary mechanism between the data source and the receiver. These intermediary packets carry redundant information that mediates the recovery process, allowing accurate data reconstruction without requiring direct retransmission of lost packets, thus reducing delay while maintaining reliability.
3Productivity
If streaming codes are used for burst loss recovery, then real-time decoding is enabled, but the theoretically assumed burst loss patterns do not match real-world videoconferencing loss patterns
Solution Approach 1:
The system dynamically changes FEC coding parameters such as code rate, block length, and segmentation based on the observed characteristics of actual packet losses in videoconferencing. This adaptability allows the streaming code to maintain real-time decoding while becoming versatile enough to handle the specific burst patterns encountered in real-world applications rather than relying on theoretical assumptions.
Solution Approach 2:
The system implements feedback mechanisms that monitor actual packet loss patterns in real-time and use this information to adjust FEC coding strategies. This feedback loop enables the system to adapt its parameters to match real-world loss patterns, improving both real-time performance and adaptability compared to fixed theoretical models.
4Productivity
If FEC-aware compression is applied to enhance packet loss recovery, then data transmission is optimized, but the system complexity increases
Solution Approach 1:
The patent merges compression and FEC encoding operations into a unified encoder architecture. By combining these functions, the system achieves FEC-aware compression that optimizes data transmission efficiency while avoiding the complexity of separate, independently optimized compression and encoding modules. The integrated design shares computational resources and coordination logic between compression and error correction functions.
Data Source
AI summary
Enhanced reliable communications systems, methods, computer program products, and integrated circuits include streaming codes for partial burst and guardspaces and synergized compression. An FEC encoder can generate two or more types of parity symbols to address partial burst and guardspace losses. An FEC encoder can utilize information from a data compressor to make frame splitting, parity symbol generation, and/or packetizing decisions. A data compressor can use information from an FEC encoder to make data compression decisions. Aspects of an FEC encoder and/or data compressor can be trained using machine learning, including reinforcement learning. Multimodal operation provides flexibility for dynamically reacting to changing communication conditions.