Real-time streaming of point cloud video, characterized by massive data volumes and high sensitivity to packet loss, remains a key challenge for immersive applications under dynamic network conditions. While connection-oriented protocols such as TCP and more modern alternatives like QUIC alleviate some transport-layer inefficiencies, including head-of-line blocking, they still retain a coarse-grained, segment-based delivery model and a centralized control loop that limit fine-grained adaptation and effective caching. We introduce INDS (Incremental Named Data Streaming), an adaptive streaming framework based on Information-Centric Networking (ICN) that rethinks delivery for hierarchical, layered media. INDS leverages the Octree structure of point cloud video and expressive content naming to support progressive, partial retrieval of enhancement layers based on consumer bandwidth and decoding capability. By combining time-windows with Group-of-Frames (GoF), INDS's naming scheme supports fine-grained in-network caching and facilitates efficient multi-user data reuse. INDS can be deployed as an overlay, remaining compatible with QUIC-based transport infrastructure as well as future Media-over-QUIC (MoQ) architectures, without requiring changes to underlying IP networks. Our prototype implementation shows up to 80% lower delay, 15-50% higher throughput, and 20-30% increased cache hit rates compared to state-of-the-art DASH-style systems. Together, these results establish INDS as a scalable, cache-friendly solution for real-time point cloud streaming under variable and lossy conditions, while its compatibility with MoQ overlays further positions it as a practical, forward-compatible architecture for emerging immersive media systems.

翻译：实时点云视频流传输具有数据量巨大、对丢包高度敏感的特点，在动态网络条件下始终是沉浸式应用面临的关键挑战。尽管面向连接的协议（如TCP）以及更现代的替代方案（如QUIC）缓解了传输层的部分低效问题（包括队头阻塞），但它们仍保留了粗粒度的、基于分段的传输模型以及集中式控制环路，限制了细粒度自适应与高效缓存。本文提出INDS（增量式命名数据流传输），这是一种基于信息中心网络（ICN）的自适应流传输框架，它重新设计了面向分层层级化媒体的传输方式。INDS利用点云视频的八叉树结构及富有表现力的内容命名机制，支持根据消费者带宽和解码能力渐进式、部分地检索增强层。通过将时间窗口与帧组（GoF）相结合，INDS的命名方案支持细粒度的网内缓存，并促进高效的多用户数据复用。INDS可作为覆盖网络部署，在无需改动底层IP网络的前提下，保持与基于QUIC的传输基础设施以及未来基于QUIC的媒体传输（MoQ）架构的兼容性。我们的原型实现表明，相较于最先进的DASH风格系统，INDS可实现高达80%的延迟降低、15-50%的吞吐量提升以及20-30%的缓存命中率提高。这些结果共同表明，INDS是一种在可变且有损网络条件下可扩展、缓存友好的实时点云流传输解决方案，同时其与MoQ覆盖网络的兼容性进一步将其定位为面向新兴沉浸式媒体系统的实用、前向兼容的架构。