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.

Load balancing plays a critical role in large-scale heterogeneous edge computing, aiming to enhance the accuracy of computing resource matching and reduce processing delays caused by imbalanced resource allocation. In highly heterogeneous distributed computing environments, scalability and accuracy in matching computing resources are essential. The Consistent Hashing (CH) algorithm is well-known for its simplicity and strong scalability, enabling extensive and flexible scheduling strategies. It achieves load balancing by evenly distributing requests among different nodes. However, the CH algorithm primarily focuses on load balancing in storage resource scenarios and often neglects load balancing for computing resources, potentially leading to reduced accuracy in matching computing resources and prolonged processing delays. This paper introduces an improved CH algorithm called chordDC, specifically designed for distributed computing scenarios. It takes into account the unified representation of user tasks and computing resources, incorporating additional discriminative criteria in the selection of computing nodes to achieve a more precise resource allocation. Through a comparative analysis with two baseline algorithms, the proposed algorithm demonstrates effective performance improvements, enhancing the accuracy of computing resource matching by 40%-50% and significantly reducing computing processing latency.