Dirk Kutscher

Our paper on INDS: Incremental Named Data Streaming for Real-Time Point Cloud Video has been accepted at ACM Multimedia 2025.

Abstract:

Real-time streaming of point cloud video – characterized by high data volumes and extreme sensitivity to packet loss – presents significant challenges under dynamic network conditions. Traditional connection-oriented protocols such as TCP/IP incur substantial retransmission overhead and head-of-line blocking under lossy conditions, while reactive adaptation approaches such as DASH lead to frequent quality fluctuations and a suboptimal user experience. In this paper, we introduce INDS (Incremental Named Data Streaming), a novel adaptive transmission framework that exploits the inherent layered encoding and hierarchical object structure of point cloud data to enable clients to selectively request enhancement layers based on available bandwidth and decoding capabilities. Built on Information-Centric Networking (ICN) principles, INDS employs a hierarchical naming scheme organized by time windows and Groups of Frames (GoF), which enhances cache reuse and facilitates efficient data sharing, ultimately reducing both network and server load. We implemented a fully functional prototype and evaluated it using emulated network scenarios. The experimental results demonstrate that INDS reduces end-to-end delay by up to 80%, boosts effective throughput by 15%–50% across diverse operating conditions, and increases cache hit rates by 20%–30% on average.

References

Ruonan Chai, Yixiang Zhu, Xinjiao Li, Jiawei Li, Zili Meng, Dirk Kutscher; INDS: Incremental Named Data Streaming for Real-Time Point Cloud Video; accepted for publication at ACM Multimedia 2025; October 2025

Our paper on ViFusion: In-Network Tensor Fusion for Scalable Video Feature Indexing has been accepted at the ACM International Conference on Multimedia Retrieval 2025 (CCF-B).

Abstract:
Large-scale video feature indexing in datacenters is critically dependent on efficient data transfer. Although in-network computation has emerged as a compelling strategy for accelerating feature extraction and reducing overhead in distributed multimedia systems, harnessing advanced networking resources at both the switch and host levels remains a formidable challenge. These difficulties are compounded by heterogeneous hardware, diverse application requirements, and complex multipath topologies. Existing methods focus primarily on optimizing inference for large neural network models using specialized collective communication libraries, which often face performance degradation in network congestion scenarios.

To overcome these limitations, we present ViFusion, a communication aware tensor fusion framework that streamlines distributed video indexing by merging numerous small feature tensors into consolidated and more manageable units. By integrating an in-network computation module and a dedicated tensor fusion mechanism within datacenter environments, ViFusion substantially improves the efficiency of video feature indexing workflows. The deployment results show that ViFusion improves the throughput of the video retrieval system by 8–22x with the same level of latency as state-of-the-art systems.

Stay tuned for the pre-print.

References

Yisu Wang, Yixiang Zhu, Dirk Kutscher; ViFusion: In-Network Tensor Fusion for Scalable Video Feature Indexing; The 15th ACM International Conference on Multimedia Retrieval; June 2025; Preprint