A Generative Artificial Intelligence–based ANFIS Approach for Low-Latency Video Communication in Cloud Environments

Abstract

Generative AI (GAI) refers to advanced models that can generate new, realistic, context-aware data or solutions by learning from existing datasets, making them extremely valuable in adaptive intelligent systems. Traditional AI technologies often face limitations, including a lack of interpretability, sensitivity to noisy data, and an inability to generalize to dynamic environments. These limitations can be effectively addressed by GAI-driven adaptive neuro-fuzzy inference systems (ANFIS). To facilitate the ease of scalable processing and real-time deployment, the proposed framework is developed in a cloud computing system, whereby the aggregation of large-scale QoE data, the training of generative models and the optimization of the fuzzy rules are handled using the cloud computing resources, and latencysensitive inference is done effectively at the edge. To address these challenges, we propose a Generative Reinforcement Learning (GRL) method that enhances decision-making skills by generating artificial experiences, utilizing the video transmission system as an environment, and learns policies to optimize latency. The Generative Diffusion Model (GDM) for feature denoising offers a powerful mechanism for removing noise in high- dimensional data, thereby enhancing the accuracy and stability of prediction tasks. Physics-Guided Generative Modeling (PG2M) combines domain-specific physics laws with AI learning to ensure scientifically consistent and interpretable outputs. Finally, Generative Adversarial Networks (GANs) are employed for generative rule evolution, combining evolutionary computation with adversarial learning to dynamically generate and improve classification rules. ANFIS utilizes fuzzy rules to model delay shapes, while GRL optimizes video transmission strategies based on the output of ANFIS. Reduce latency dynamically by learning adaptive frame scheduling; it improves network responsiveness to fluctuations. The presented method achieved 94% accuracy and improved the latency of the video communication service.