# MCUCoder: Adaptive Bitrate Learned Video Compression for IoT Devices

arXiv: https://arxiv.org/abs/2411.19442 · HTML: https://mlsanity.com/paper/2411.19442/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 60 references that are in the map.
- First posted on arXiv 29 Nov 2024.

## Builds on (most cited first, 5 of 5)

- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [VCT: A Video Compression Transformer](https://mlsanity.com/paper/2206.07307/) (2022)
- [Instance-Adaptive Video Compression: Improving Neural Codecs by Training on the Test Set](https://mlsanity.com/paper/2111.10302/) (2021)
- [HydraViT: Stacking Heads for a Scalable ViT](https://mlsanity.com/paper/2409.17978/) (2024)
- [Millimeter-Scale Ultra-Low-Power Imaging System for Intelligent Edge Monitoring](https://mlsanity.com/paper/2203.04496/) (2022)

## Built on by (most cited first, 2 of 2)

- [Efficient training for compact compression models via sequential distillation](https://mlsanity.com/paper/2601.05639/) (2026)
- [SEAOTTER: Sensor Embedded Autoencoding with One-Time Transcode for Efficient Reconstruction](https://mlsanity.com/paper/2606.03940/) (2026)

## Abstract (arXiv)

The rapid growth of camera-based IoT devices demands the need for efficient video compression, particularly for edge applications where devices face hardware constraints, often with only 1 or 2 MB of RAM and unstable internet connections. Traditional and deep video compression methods are designed for high-end hardware, exceeding the capabilities of these constrained devices. Consequently, video compression in these scenarios is often limited to M-JPEG due to its high hardware efficiency and low complexity. This paper introduces , an open-source adaptive bitrate video compression model tailored for resource-limited IoT settings. MCUCoder features an ultra-lightweight encoder with only 10.5K parameters and a minimal 350KB memory footprint, making it well-suited for edge devices and MCUs. While MCUCoder uses a similar amount of energy as M-JPEG, it reduces bitrate by 55.65% on the MCL-JCV dataset and 55.59% on the UVG dataset, measured in MS-SSIM. Moreover, MCUCoder supports adaptive bitrate streaming by generating a latent representation that is sorted by importance, allowing transmission based on available bandwidth. This ensures smooth real-time video transmission even under fluctuating network conditions on low-resource devices. Source code available at https://github.com/ds-kiel/MCUCoder.