Semantic-Aware Adaptive Video Streaming Using Latent Diffusion Models for Wireless Networks

TL;DR

This paper introduces a semantic-aware adaptive video streaming framework using Latent Diffusion Models that significantly reduces bandwidth and storage needs while maintaining high visual quality in wireless networks.

Contribution

It integrates Latent Diffusion Models into real-time video streaming to improve compression and semantic transmission, enhancing QoE and resource efficiency over traditional methods.

Findings

Achieves higher QoE compared to state-of-the-art solutions.

Reduces bandwidth and storage requirements significantly.

Restores temporal coherence effectively in noisy environments.

Abstract

This paper proposes a novel Semantic Communication (SemCom) 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 retaining B-frames and P-frames as adjustment metadata to support efficient refinement of video reconstruction at the user side, the proposed framework further incorporates 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.