Kodo is a high-performance erasure coding library with a special focus on network coding algorithms and codecs. Kodo is written in C++ and it was designed to ensure high performance, testability, flexibility and extendability.

A fast implementation of finite fields is a prerequisite for fast RLNC and other linear block codes. Kodo relies on our optimized Fifi library which supports multiple finite fields with different sizes.

The Kodo libraries expose a simple Application Programming Interface (API) which allows a programmer to take advantage of network coding in a custom application. Kodo supports various network coding codecs e.g. standard Random Linear Network Coding (RLNC), Systematic RLNC, Sparse RLNC. Each algorithm offers unique advantages and drawbacks as well as a different set of parameters that can be tuned in real time. For example, this can be employed to adapt to varying cellular channels, to change the amount of redundancy in a distributed storage network, or to adapt to node failures in meshed networks.

For researchers, Kodo’s layered structure greatly simplifies the implementation of new and experimental RLNC variants, since typically only a new layer needs to be added. For example, this can be exploited to develop codes with special structures targeted and optimized towards specific uses, e.g. audio and video streaming, meshed networks and distributed storage.


The Kodo libraries are implemented in several projects and each project has its own git repository. It is recommended to choose a single project and work with the API that is exposed by that project.

The kodo-cpp library defines a simple, high-level C++ API to conveniently access the basic functionality of Kodo, such as encoding and decoding data with various codecs. It is very easy to integrate kodo-cpp into your C++ project, so it is the recommended option for most users.
The kodo-c library provides a simple C API that allows the programmer to use Kodo in a C program. The C API also enables interoperability with other programming languages that are not directly supported by the Kodo libraries.
The kodo-rlnc project provides low-level access to the underlying C++ implementation of the various RLNC codecs. This enables the developer to customize the operation of these codecs or create new variants, but working with the low-level details might present some difficulties. This option is only recommended for experienced C++ programmers.
This library provides high-level Python bindings for Kodo. The simple examples demonstrate how to use the basic functionality of Kodo through the Python API. This option is recommended for Python-based projects and for programmers who are not familiar with C++.
This project contains examples for using Kodo with the ns-3 network simulator. This can be a great starting point for researchers who are mainly interested in network simulations.


One of the most prominent features of Network Coding is the possibility to use coding at the intermediate network nodes (recoding) and not only at the sender (encoding) and the receiver (decoding).
Systematic coding
The sender can send some or all of the original symbols within a given block uncoded. Coded packets can be generated later to repair any packet losses. Systematic coding is useful in simple topologies as it increases the decoding throughput and decreases the coding overhead.
On-the-fly coding
The sender can encode over a growing block of data. This is useful for live content where the data becomes available over time, potentially at a variable rate.
Partial decoding
The receiver can decode some of the original symbols before the entire data block is decoded. This approach is more compatible with error-resilient codecs (video, audio) as instead of receiving the whole data block or nothing, a partial data block can be retrieved.
Variable symbol length (coming soon)
The symbols in a block of coded data can have different lengths. The fragmentation and aggregation is handled transparently by the library. This can be useful when the size of the incoming data packets is variable.
Real-time adjustable density
The density at the sender can be adjusted in real time which permits adaptation to changing network conditions.
Symbol pruning
The encoder can drop certain symbols which were already decoded at the decoder.
File encoder
The sender can directly encode data files that are automatically split into generations.
Zero-copy API
The encoder and decoder can operate directly on user provided buffers, eliminating the need for costly copy operations.
Object pooling
The library can re-use existing encoder and decoder instances to facilitate efficient memory management.
Hardware optimized (on select hardware)
Optimizations for various CPU architectures, using SIMD instructions and various coding algorithms to provide the best performance.

Random Linear Network Coding codecs

Standard RLNC
All symbols are combined uniformly at random. In general, this type of coding is “dense”, since the symbols in the data block are mixed as much as possible. Density is lower for small field sizes.
Sparse RLNC with uniform density
Some symbols are excluded with a given probability during encoding. The remaining symbols are combined as in the standard RLNC case. This is typically useful when the block size is very high. The density can be reduced significantly without any negative effect and the decoding throughput can be increased substantially at the same time.
Sparse RLNC with fixed density
A fixed number of symbols are combined at random. This can be used when feedback is available from the decoder. The encoding process can be tuned at the encoder according to the state of the decoder.
Seed-based RLNC
Instead of sending the full coding vector, a small random seed can be sent to generate the coding vector. This reduces the overhead, but makes recoding difficult and in some cases impossible. This is typically used when recoding is not necessary or used very sparingly.
On-the-fly RLNC
Symbols can be encoded as they are made available and data is released from the decoder as decoding progresses. This is different from traditional block codes where all data has to be available before encoding or decoding takes place. This codec is well suited for low-delay services such as messaging, voice over IP or video streaming.
Perpetual RLNC
A sparse and structured code where the non-zero coding coefficients are localized to a specific part of the coding vector. The width of this non-zero part is analogous to the density parameter of random sparse codes. This approach allows for structured decoding, which can yield a substantially higher throughput than random sparse codes, especially for large generation sizes.
Fulcrum RLNC
The Fulcrum network codes use a concatenated code structure with an “outer” and “inner” code. They provide an end-to-end performance that is close to that of a large field size network coding system for high–end receivers, while simultaneously catering to low–end ones that can only decode in GF(2). For a detailed description of the Fulcrum codec, see the following paper by Lucani et. al.

Other codecs and approaches

Reed-Solomon code
Traditional Reed-Solomon (RS) code which does not support recoding. The current implementation uses a systematic Vandermonde matrix as described in RFC 5510.
Carousel code
Also called a repetition code, the data is simply transmitted in a round-robin fashion. This code is mostly useful for simulation purposes and performance evaluations. Furthermore it can be used to provide the Compact No-Code scheme described in RFC 5445.
Random Annex overlay code
Enables mixing of several generations. By using multi-stage decoding, this technique can offer increased decoding throughput at the cost of increased decoding delay. The Random Annex code is useful in scenarios where large objects need to be transmitted in a feedback-constrained system (feedback is expensive or impossible) and where using a single large generation is not feasible.

Platform Support

Kodo is portable to a wide range of platforms. The Kodo Specifications page provides an overview of the supported platforms and compilers.

We ensure compatibility with the supported platforms through a suite of unit tests, the current status can be checked at the Steinwurf Buildbot page. At the bottom of the main page, you can find detailed information about which platforms and compilers are currently tested by Steinwurf.


The Buildbot is used for several different libraries. The Kodo library can be found in the overview on the main page.