Performance Evaluation of Different Optimization Techniques for TMIV Immersive Video Streaming

Cloud-based immersive video streaming is a growing technology that has the potential to revolutionize how we consume entertainment and media. However, some challenges need to be addressed, such as delivering high-quality video to users with internet speed and processing capabilities. One way to tackle these obstacles is by optimizing the configuration of Three-Dimensional Image View (TMIV) for encoding and streaming videos in cloud environments. This study thoroughly compares optimization techniques including learning, reinforcement learning, and heuristic methods. The authors also investigate the impact of factors like usage decoding time and video quality. To effectively evaluate their proposed approach, diverse video datasets and different streaming conditions are utilized. The findings demonstrate improved decoding time and video quality utilization compared to traditional TMIV optimization methods. As a result, this technique holds the potential for enhancing the experience of immersive video streaming in applications such as virtual reality, augmented reality, and 360° videos, which are examples of immersive digital experiences. Furthermore, it offers the added benefit of reducing both requirements and computational resources needed for streaming videos—thus enabling better accessibility for users with limited resources.