Title: Low-complexity Deep Video Compression with A Distributed Coding Architecture. Abstract: Prevalent video compression methods follow a $predictive\;coding$ architecture that relies on a heavy encoder to exploit the statistical redundancy, which makes it challenging to deploy them on resource-constrained devices. Meanwhile, as early as the 1970s, distributed source coding theory, namely, Slepian-Wolf and Wyner-Ziv theorems, has indicated that efficient compression of correlated sources can be achieved by exploiting the source statistics at the decoder only, with the help of effective side information (SI). This has inspired a $distributed\;coding$ architecture that is promising to reduce the encoder complexity. While there have been some attempts to develop practical distributed video coding systems, traditional methods suffer from a substantial performance gap to the predictive coding architecture. Inspired by the recent successes of deep learning in enhancing image and video compression, we propose the first end-to-end distributed deep video compression (Distributed DVC) framework with neural network-based modules that can be optimized to improve the rate-distortion performance. A key ingredient is an effective SI generation module at the decoder, which helps to effectively exploit the inter-frame correlation without computation-intensive encoder-side motion estimation and compensation. Experiments show that Distributed DVC significantly outperforms conventional distributed video coding methods and H.264. Meanwhile, it enjoys $6\sim7$ times encoding speedup against DVC with only 1.61\% increase in the bitrate for 1080P test videos on the UVG dataset.