DT-CNST: Deterministic Transmission Based on Computing-Network-Storage Resources Tradeoff for Real-Time Holographic-Type Communication
Xu Huang, Jia Chen, Deyun Gao, Shang Liu, Dongsheng Qian, Chenxi Liao, Jing Chen, Hongke Zhang · IEEE Transactions on Network Science and Engineering · 2025
With the rise of the Metaverse, Real-time Holographic-type Communication (HTC) has emerged as a promising approach to creating immersive experiences. In HTC, Motion-to-Photon (MTP) latency, which is composed of computing, network and storage latency, must be ultra-low and bounded to avoid vertigo syndrome. Time Sensitive Networks (TSN) and Deterministic Networks (DetNet) are generally adopted to ensure deterministic transmission with ultra-low bounded latency. However, the transmission of HTC streaming faces computing latency fluctuation and requires high bandwidth, which exceeds the capabilities of TSN and DetNet. Therefore, to address these limitations, this paper proposes Deterministic Transmission based on Computing-Network-Storage Resources Tradeoff (DT-CNST). In DT-CNST, a Computing-based Traffic Shaper is proposed to eliminate computing latency fluctuation. A Cyclic Buffering, Queuing and Forwarding mechanism is also designed to ensure deterministic transmission of large frames. Moreover, a Computing-Network-Storage (CNS) model is established, incorporating constraints and a resource tradeoff optimization. Based on the CNS model, the Hybrid PPO-Greedy Resource Tradeoff (HPG-RT) algorithm is developed to maximize the number of scheduled streams. Simulation results show that HPG-RT achieves a schedulability rate of 90.7%, outperforming benchmarks with a maximum improvement of 35.5%. Prototype results confirm that DT-CNST provides an immersive experience with at least 25 frames per second.