Hybrid 5G–Satellite Adaptive Video Streaming for Low-Latency Cloud-Based Cooperative Perception

Abstract

The growing complexity of autonomous and connected vehicle systems necessitates reliable, low-latency communication between vehicles, infrastructure, and cloud resources. This dissertation presents a comprehensive framework for optimizing real-time data transmission in cloud-based cooperative perception systems (CPS), focusing on adaptive video compression, hybrid networking, and cloud-based inference. The research aims to reduce end-to-end latency while maintaining high perception accuracy and connectivity reliability across heterogeneous communication environments. The proposed architecture integrates adaptive H.265 (HEVC) compression, hybrid 5G–Starlink networking, and GPU-accelerated inference to support scalable roadside unit (RSU) camera deployments. An adaptive compression controller dynamically adjusts frame rate, resolution, and bitrate in response to bandwidth fluctuations, while a hybrid link manager employs make-before-break switching and multipath transmission to sustain continuous connectivity. The system was evaluated through a combination of simulation, laboratory bench testing, and real-world field experiments under diverse network conditions and video workloads. Results demonstrate that the integrated approach achieves end-to-end latency below 100 milliseconds and connectivity uptime exceeding 99 percent, outperforming conventional single-network and fixed-parameter configurations. Statistical analyses confirm significant reductions in latency variance, frame loss, and recovery time following network disruptions. Moreover, the proposed methods maintain detection accuracy (mAP > 0.75) and system stability under high-load scenarios involving up to 100 concurrent RSU streams. This work contributes (i) a validated, modular framework for real-time cloud-assisted cooperative perception, (ii) empirical benchmarks quantifying compression–network trade-offs, and (iii) deployment guidelines for scalable CPS implementations in intelligent transportation systems. Collectively, these contributions advance the state of knowledge toward deployable, cloud-integrated Level-5 autonomous driving infrastructures, enabling robust perception pipelines with demonstrable reliability, scalability, and cost efficiency.