arXiv:2502.05695v1 Announce Type: new
Abstract: This paper proposes a novel framework for real-time adaptive-bitrate video streaming by integrating latent diffusion models (LDMs) within the FFmpeg techniques. This solution addresses the challenges of high bandwidth usage, storage inefficiencies, and quality of experience (QoE) degradation associated with traditional constant bitrate streaming (CBS) and adaptive bitrate streaming (ABS). The proposed approach leverages LDMs to compress I-frames into a latent space, offering significant storage and semantic transmission savings without sacrificing high visual quality. While it keeps B-frames and P-frames as adjustment metadata to ensure efficient video reconstruction at the user side, the proposed framework is complemented with the most state-of-the-art denoising and video frame interpolation (VFI) techniques. These techniques mitigate semantic ambiguity and restore temporal coherence between frames, even in noisy wireless communication environments. Experimental results demonstrate the proposed method achieves high-quality video streaming with optimized bandwidth usage, outperforming state-of-the-art solutions in terms of QoE and resource efficiency. This work opens new possibilities for scalable real-time video streaming in 5G and future post-5G networks.
New Framework for Real-Time Adaptive-Bitrate Video Streaming: A Multi-disciplinary Approach
Video streaming has become an integral part of our daily lives, and the demand for high-quality video content is increasing exponentially. However, traditional streaming methods face challenges such as high bandwidth usage, storage inefficiencies, and degradation of quality of experience (QoE). In this paper, a novel framework is proposed to address these challenges by integrating latent diffusion models (LDMs) within the FFmpeg techniques.
One of the key contributions of this framework is the use of LDMs to compress I-frames into a latent space. By leveraging latent diffusion models, significant storage and semantic transmission savings can be achieved without sacrificing visual quality. This is crucial in modern multimedia information systems, where efficient storage and transmission are vital.
Furthermore, the proposed framework considers the multi-disciplinary nature of video streaming by incorporating state-of-the-art denoising and video frame interpolation (VFI) techniques. These techniques help mitigate semantic ambiguity and restore temporal coherence between frames, even in noisy wireless communication environments. By addressing temporal coherence, the framework ensures a smooth and seamless video streaming experience.
From a wider perspective, this research aligns with the field of Artificial Reality, Augmented Reality, and Virtual Realities. The integration of LDMs, denoising, and VFI techniques in video streaming has potential applications in these fields. For example, in augmented reality, the reduction of semantic ambiguity can enhance the accuracy and realism of virtual objects overlaid onto the real world.
This novel framework also has implications for 5G and future post-5G networks. As video streaming becomes more prevalent with the advent of faster network technologies, resource efficiency becomes crucial. The proposed method not only achieves high-quality video streaming but also optimizes bandwidth usage, making it well-suited for scalable real-time video streaming in these networks.
In conclusion, this paper introduces a groundbreaking framework for real-time adaptive-bitrate video streaming. By leveraging latent diffusion models, denoising, and video frame interpolation techniques, this framework tackles the challenges of traditional streaming methods and opens up new possibilities for the multimedia information systems, artificial reality, augmented reality, and virtual realities. As technology continues to evolve, this research paves the way for more efficient and immersive video streaming experiences.