Code On provides a commercial‑grade Random Linear Network Coding (RLNC) software library that adds forward error correction to multicast, multipath, IoT mesh, and storage networks without requiring flow coordination.
Funding
Funding not disclosed
Founders
Product
Problem
Data transmission over multicast, multipath, IoT mesh, and storage networks often suffers from packet loss, high latency, and complex coordination requirements for forward error correction, limiting reliability and efficiency of satellite, 5G, connected‑car, video streaming, and storage applications.
Solution
Code On offers a commercial‑grade Random Linear Network Coding (RLNC) software library that provides forward error correction without the need for flow coordination. The library can be integrated into existing network stacks or storage systems to improve packet reliability, reduce retransmission latency, and enable seamless combination of variable‑rate flows. By applying linear combinations of data packets, RLNC allows receivers to recover lost information from any subset of transmitted packets, simplifying multicast distribution, multipath routing, and mesh network dissemination. The technology is applicable across satellite links, 5G mobile backhaul, connected‑vehicle communications, video broadcast, over‑the‑air updates, and storage arrays at the drive, SAN, or cloud level.
Target Audience
Primary customers are network equipment manufacturers, telecom operators, satellite service providers, automotive connectivity platforms, video streaming services, and storage system vendors seeking to improve data reliability and efficiency.
Features
- Commercial‑grade RLNC library with APIs for easy integration into network and storage software
- Forward error correction that works without coordination between parallel flows or multicast groups
- Support for variable‑rate multipath aggregation, enabling seamless bandwidth utilization
- Enhanced reliability and reduced latency for satellite, 5G, connected‑car, and video streaming links
- Data repair and recovery capabilities for drive, SAN, and cloud storage systems
- Tools and documentation for training engineering teams on RLNC deployment