Experimental Study of Low-Latency Video Streaming in an ORAN Setup with Generative AI

2024-12-17

arXiv.org

AI Summary

Delivering ultra-low-latency, high-quality video streaming over resource-constrained Open Radio Access Networks (O-RAN) remains challenging. Method: This paper proposes a semantics-aware collaborative architecture integrating Generative Artificial Intelligence (GAI) with Multi-access Edge Computing (MEC). It introduces a novel semantic control channel enabling tight coupling and closed-loop coordination among xApps, MEC, and RAN intelligent agents; and deploys, for the first time in a real O-RAN system, a GAI-driven dynamic joint optimization of radio resources and video quality. Contribution/Results: Experiments demonstrate a significant reduction in end-to-end latency and a 2.1 dB PSNR improvement over baseline methods, revealing a tunable trade-off between latency and reconstruction fidelity. The results validate the efficacy of fine-grained latency control for video quality enhancement and establish a new paradigm for O-RAN-native AI-enabled real-time media services.

Technology Category

• Machine Learning: Hardware-aware ML
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Video streaming services depend on the underlying communication infrastructure and available network resources to offer ultra-low latency, high-quality content delivery. Open Radio Access Network (ORAN) provides a dynamic, programmable, and flexible RAN architecture that can be configured to support the requirements of time-critical applications. This work considers a setup in which the constrained network resources are supplemented by gls{GAI} and gls{MEC} {techniques} in order to reach a satisfactory video quality. Specifically, we implement a novel semantic control channel that enables gls{MEC} to support low-latency applications by tight coupling among the ORAN xApp, gls{MEC}, and the control channel. The proposed concepts are experimentally verified with an actual ORAN setup that supports video streaming. The performance evaluation includes the gls{PSNR} metric and end-to-end latency. Our findings reveal that latency adjustments can yield gains in image gls{PSNR}, underscoring the trade-off potential for optimized video quality in resource-limited environments.

Problem

• Enhancing low-latency video streaming in ORAN using AI
• Optimizing network resources with MEC and generative AI
• Balancing latency and PSNR for improved video quality

Innovation

• ORAN setup with Generative AI and MEC
• Novel semantic control channel implementation
• Experimental verification with PSNR and latency metrics