Streaming 3D Gaussian Splatting requires highly scalable, progressive representations. Existing progressive methods rely on \textit{discrete layering}, accumulating separate splat sets for each level of detail. This structural independence between layers inherently leads to error accumulation, severe splat redundancy, and uncontrolled quality transitions. We propose EvoGS, the first \textit{continuous-layering} representation. Organized as an Evolution Tree, EvoGS generates finer details via an explicit, wavelet-inspired parent-child refinement. This empowers child nodes to structurally correct ancestral errors, yield inherently sparse and highly compressible inter-layer signals. Extensive experiments show EvoGS eliminates splat redundancy from over 65\% to under 25\%. Compared to state-of-the-art baselines, it reduces transmission payload and GPU VRAM footprint by up to 2.4$\times$ and 5.5$\times$, respectively, and achieves smooth quality transitions optimal for real-time adaptive streaming. Project page: https://yuang-ian.github.io/evogs/

翻译：流式传输3D高斯溅射需要高度可扩展的渐进式表示。现有渐进方法依赖离散分层，为每个细节层级累积独立的高斯集合。这种层间的结构独立性固化了误差累积、严重的点云冗余以及不可控的质量过渡。我们提出EvoGS——首个连续分层表示。以演化树为组织架构，EvoGS通过显式的类小波父子细化机制生成更精细的细节。这使得子节点能够结构性地修正祖先误差，产生本质稀疏且高度可压缩的层间信号。大量实验表明，EvoGS将点云冗余度从65%以上降至25%以下。与最新基线方法相比，其传输负载和GPU显存占用分别降低高达2.4倍与5.5倍，并实现了适用于实时自适应流传输的最优平滑质量过渡。项目主页：https://yuang-ian.github.io/evogs/