SAW: Semantic-Aware WebRTC Transmission Using Diffusion-Based Scalable Video Coding

As video transmission systems expand into various complex scenarios, real-time video coding methods are essential for maintaining low latency and high perceptual quality across varying network conditions. In this work, we propose service-aware Web real-time communication (WebRTC), a semantic-assisted WebRTC system built on scalable video coding (SVC). Specifically, this system is structured with three layers: 1) $\mathcal {L}_{1}$ extracts and down-samples semantic information at the encoder, employing a novel super-resolution (SR) method named BUS-DDIM at the decoder to enhance the transmission efficiency and machine vision recognition rate; 2) $\mathcal {L}_{2}$ adaptively compresses high-quality video by discarding frames with little motion at the encoder to address latency issues under poor network conditions, and utilize the adjacent frame-guided denoised interpolation model called the adjacent frame-guided denoised diffusion implicit model for restoring the video; and 3) $\mathcal {L}_{3}$ transmits high-quality video tailored for users with high-definition video requirements and favorable network conditions. These layers dynamically enhance the visual experience and ensure low latency across various network environments. Experiments are conducted on diverse videos to validate the effectiveness of the proposed framework. The performance evaluation under real-time scenarios indicates significant enhancements in video quality and transmission efficiency, showcasing compatibility and versatility across various applications.