Argus: Bandwidth-Efficient Live Multiview Video Streaming via Sparse-View Gaussian Reconstruction

Multiview video streaming requires much higher bandwidth than conventional 2D video streaming. Current multi-view video streaming systems mainly leverage view interpolation to reduce redundancy between adjacent 2D views, and do not consider 3D spatial redundancy among all views. In this paper, we propose Argus, a multiview video streaming system that reduces 3D spatial redundancy by transmitting only a sparse subset of views and synthesizing the remaining views through 3D Gaussian reconstruction and splatting. Specifically, we propose: (i) a 3D Gaussian splatting-assisted multiview video coding method featuring real-time 3D Gaussian reconstruction and view synthesis, and (ii) a content-adaptive view selection method that dynamically selects a subset of views to optimize the visual quality of the synthesized views. We develop a prototype system of Argus, supporting up to 50-view autostereoscopic 3D display. Comprehensive experiments show that Argus achieves bitrate reduction by 41.33% and 44.12% compared with two baselines on two datasets, respectively, and supports real-time multiview video streaming at over 30 FPS.