Enhancing Cloud-Based Video Streaming Efficiency using Neural Networks

The growing demand for high-quality video streaming services has increased the need for efficient utilization of cloud resources. This research proposes a novel solution to this challenge by utilizing neural networks to enhance video streaming performance in the cloud. Specifically, the research introduces a neural network architecture capable of dynamically adjusting video quality based on network conditions and user preferences. By combining convolutional and recurrent neural networks, the proposed model learns patterns in video streams and predicts the optimal bit rate for each frame. The architecture also considers network conditions such as bandwidth, latency, and packet loss to adapt video quality in real-time. The proposed approach aims to improve quality of service while reducing cloud resource utilization, leading to cost-effective and scalable video streaming services. Extensive simulations and experiments were conducted to evaluate the feasibility and effectiveness of the approach. The proposed neural network architecture achieved up to 30% and 40% higher PSNR and SSIM scores, respectively, than the baseline approach. These results demonstrate the effectiveness of the proposed approach in enhancing video streaming efficiency in the cloud. Overall, this research provides valuable insights into designing efficient video streaming systems and highlights the potential benefits of neural networks in cloud-based video streaming services.