INDS: Incremental Named Data Streaming for Real-Time Point Cloud Video

2025-08-19

AI Summary

Real-time point cloud video streaming suffers from high bandwidth consumption and packet-loss sensitivity under dynamic network conditions; existing TCP/QUIC protocols exhibit insufficient fine-grained adaptability and cache efficiency due to fragmented transmission and centralized control. To address these issues, this paper proposes an adaptive streaming framework built upon Named Data Networking (NDN). Our approach innovatively integrates octree-based content structuring with expressive naming, enabling incremental retrieval of geometric and attribute layers. We further introduce a Group-of-Frames (GoF) scheduling strategy coordinated with temporal windows to enhance multi-user cache reuse. The framework remains compatible with Media-over-QUIC architectures. Experimental results demonstrate an 80% reduction in end-to-end latency, a 15–50% throughput improvement, and a 20–30% increase in cache hit rate—significantly outperforming DASH-based baselines.

Technology Category

• Data Mining & Knowledge Management: Data Stream Mining
• Search and Optimization: Distributed Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Security and Privacy: Data transparency and provenance
• Responsible Web: Consent frameworks and practices on the web

Abstract

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.

Problem

• Real-time streaming of massive point cloud video data
• Overcoming limitations of segment-based delivery and centralized control
• Enabling fine-grained adaptation and effective caching for immersive applications

Innovation

• Incremental Named Data Streaming for adaptive delivery
• Leverages Octree structure for progressive partial retrieval
• Combines time-windows with Group-of-Frames naming