# Wireless Semantic Communications for Video Conferencing

arXiv: https://arxiv.org/abs/2204.07790 · HTML: https://mlsanity.com/paper/2204.07790/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2022.

## Built on by (most cited first, 30 of 50)

- [Generative Semantic Communication: Diffusion Models Beyond Bit Recovery](https://mlsanity.com/paper/2306.04321/) (2023)
- [Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Data](https://mlsanity.com/paper/2205.00271/) (2022)
- [Machine Learning for Large-Scale Optimization in 6G Wireless Networks](https://mlsanity.com/paper/2301.03377/) (2023)
- [OFDM-Based Digital Semantic Communication with Importance Awareness](https://mlsanity.com/paper/2401.02178/) (2024)
- [Is Semantic Communications Secure? A Tale of Multi-Domain Adversarial Attacks](https://mlsanity.com/paper/2212.10438/) (2022)
- [Vulnerabilities of Deep Learning-Driven Semantic Communications to Backdoor (Trojan) Attacks](https://mlsanity.com/paper/2212.11205/) (2022)
- [Age of Information in Deep Learning-Driven Task-Oriented Communications](https://mlsanity.com/paper/2301.04298/) (2023)
- [Reasoning over the Air: A Reasoning-based Implicit Semantic-Aware Communication Framework](https://mlsanity.com/paper/2306.11229/) (2023)
- [Robust Image Semantic Coding with Learnable CSI Fusion Masking over MIMO Fading Channels](https://mlsanity.com/paper/2406.07389/) (2024)
- [Optimization of Image Transmission in a Cooperative Semantic Communication Networks](https://mlsanity.com/paper/2301.00433/) (2023)
- [Importance-Aware Image Segmentation-based Semantic Communication for Autonomous Driving](https://mlsanity.com/paper/2401.10153/) (2024)
- [Tackling Distribution Shifts in Task-Oriented Communication with Information Bottleneck](https://mlsanity.com/paper/2405.09514/) (2024)
- [Synchronous Multi-modal Semantic Communication System with Packet-level Coding](https://mlsanity.com/paper/2408.04535/) (2024)
- [Deep Learning in Physical Layer: Review on Data Driven End-to-End Communication Systems and their Enabling Semantic Applications](https://mlsanity.com/paper/2401.12800/) (2024)
- [Communication-Efficient Framework for Distributed Image Semantic Wireless Transmission](https://mlsanity.com/paper/2308.03713/) (2023)
- [Multi-Receiver Task-Oriented Communications via Multi-Task Deep Learning](https://mlsanity.com/paper/2308.06884/) (2023)
- [Integrating Pre-Trained Language Model with Physical Layer Communications](https://mlsanity.com/paper/2402.11656/) (2024)
- [Distortion Resilience for Goal-Oriented Semantic Communication](https://mlsanity.com/paper/2309.14587/) (2023)
- [Knowledge Enhanced Semantic Communication Receiver](https://mlsanity.com/paper/2302.07727/) (2023)
- [Training-Free Multi-User Generative Semantic Communications via Null-Space Diffusion Sampling](https://mlsanity.com/paper/2405.09866/) (2024)
- [Promptus: Can Prompts Streaming Replace Video Streaming with Stable Diffusion](https://mlsanity.com/paper/2405.20032/) (2024)
- [VideoQA-SC: Adaptive Semantic Communication for Video Question Answering](https://mlsanity.com/paper/2406.18538/) (2024)
- [Goal-Oriented Semantic Communication for Wireless Visual Question Answering](https://mlsanity.com/paper/2411.02452/) (2024)
- [Editable-DeepSC: Reliable Cross-Modal Semantic Communications for Facial Editing](https://mlsanity.com/paper/2411.15702/) (2024)
- [Multi-Task Semantic Communications via Large Models](https://mlsanity.com/paper/2503.22064/) (2025)
- [Multi-User Semantic Fusion for Semantic Communications over Degraded Broadcast Channels](https://mlsanity.com/paper/2406.10556/) (2024)
- [Semantic Communication for Cooperative Perception using HARQ](https://mlsanity.com/paper/2409.09042/) (2024)
- [A Semantic Communication System for Real-time 3D Reconstruction Tasks](https://mlsanity.com/paper/2412.01191/) (2024)
- [WVSC: Wireless Video Semantic Communication with Multi-frame Compensation](https://mlsanity.com/paper/2503.21197/) (2025)
- [Conquering High Packet-Loss Erasure: MoE Swin Transformer-Based Video Semantic Communication](https://mlsanity.com/paper/2508.01205/) (2025)

## Abstract (arXiv)

Video conferencing has become a popular mode of meeting even if it consumes considerable communication resources. Conventional video compression causes resolution reduction under limited bandwidth. Semantic video conferencing maintains high resolution by transmitting some keypoints to represent motions because the background is almost static, and the speakers do not change often. However, the study on the impact of the transmission errors on keypoints is limited. In this paper, we initially establish a basal semantic video conferencing (SVC) network, which dramatically reduces transmission resources while only losing detailed expressions. The transmission errors in SVC only lead to a changed expression, whereas those in the conventional methods destroy pixels directly. However, the conventional error detector, such as the cyclic redundancy check, cannot reflect the degree of expression changes. To overcome this issue, we develop an incremental redundancy hybrid automatic repeat-request (IR-HARQ) framework for the varying channels (SVC-HARQ) incorporating a novel semantic error detector. The SVC-HARQ has flexibility in bit consumption and achieves good performance. In addition, SVC-CSI is designed for channel state information (CSI) feedback to allocate the keypoint transmission and enhance the performance dramatically. Simulation shows that the proposed wireless semantic communication system can significantly improve the transmission efficiency.This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible