INDS: Incremental Named Data Streaming for Real-Time Point Cloud Video
Ruonan Chai, Yixiang Zhu, Xinjiao Li, Jiawei Li, Zili Meng, Dirk Kutscher

TL;DR
INDS is an adaptive, ICN-based streaming framework for real-time point cloud video that improves delay, throughput, and cache efficiency by leveraging hierarchical data structures and progressive retrieval.
Contribution
The paper introduces INDS, a novel ICN-based streaming approach that supports fine-grained, progressive delivery and caching for point cloud videos, compatible with existing transport protocols.
Findings
Up to 80% lower delay compared to DASH
15-50% higher throughput
20-30% increased cache hit rates
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…
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