Scalable Video Streaming Solutions Using Federated Learning

The proliferation of video streaming services demands scalable solutions that can adapt to diverse user requirements and network conditions while maintaining privacy and efficiency. This research explores the application of Federated Learning (FL) as a paradigm to address these challenges. We propose a novel FL-based framework for scalable video streaming, focusing on optimizing streaming quality and bandwidth utilization across heterogeneous devices and networks. Our framework leverages decentralized data processing and model training across multiple nodes, reducing the need for centralized data collection and thereby enhancing privacy. We introduce an adaptive algorithm within the FL framework that dynamically adjusts streaming parameters based on user context, network conditions, and device capabilities. This approach ensures optimal video quality and efficient bandwidth usage without compromising user privacy. To validate the efficacy of our proposed solution, we conducted extensive experiments using a simulated environment that mimics real-world streaming scenarios with varying network conditions and device types. The results demonstrate a significant improvement in streaming efficiency and quality. Specifically, our FL-based framework achieved a 25% reduction in bandwidth usage compared to traditional streaming methods, while maintaining or enhancing video quality. Additionally, the user experience, measured through Quality of Experience (QoE) metrics, showed an average improvement of 18%, indicating not only a more efficient but also a more satisfactory streaming experience for users.