LTS: A DASH Streaming System for Dynamic Multi-Layer 3D Gaussian Splatting Scenes

We present a novel DASH-based streaming system for dynamic 3D Gaussian Splatting (3DGS) scenes, addressing the challenges of streaming large amounts of 3DGS data over diverse and dynamic networks. Our Layer, Tile, and Segment Adaptive streaming (LTS) system combines three key features: (i) multi-layer streaming, which adapts to diverse client capabilities while balancing visual quality and bandwidth usage, (ii) tiled streaming, which reduces unnecessary data transmission by focusing on the user's viewport, and (iii) segment streaming, which divides dynamic 3DGS scenes into segments, letting clients request them dynamically to handle network fluctuations. Our experimental results demonstrate that our LTS system achieves superior performance in both live and on-demand streaming of dynamic 3DGS scenes compared to the baselines. For example, in live streaming, LTS could achieve up to 99.70% reduction in missing frames on average and deliver a maximum PSNR (Peak Signal-to-Noise Ratio) improvement of 10.08 dB. In on-demand streaming, LTS could reduce the freeze time by up to 92.01%, and increase the synthesized view quality by up to 5.14 dB in PSNR and 0.11 in SSIM (Structural Similarity Index). Our source codes are available at: https://github.com/AIINS-NTHU/LTS-DASH-Streaming-System-for-3DGS.