This paper investigates the adaptive bitrate (ABR) video semantic communication over wireless networks. In the considered model, video sensing devices must transmit video semantic information to an edge server, to facilitate ubiquitous video sensing services such as road environment monitoring at the edge server in autonomous driving scenario. However, due to the varying wireless network conditions, it is challenging to guarantee both low transmission delay and high semantic accuracy at the same time if devices continuously transmit a fixed bitrate video semantic information. To address this challenge, we develop an adaptive bitrate video semantic communication (ABRVSC) system, in which devices adaptively adjust the bitrate of video semantic information according to network conditions. Specifically, we first define the quality of experience (QoE) for video semantic communication. Subsequently, a swin transformer-based semantic codec is proposed to extract semantic information with considering the influence of QoE. Then, we propose an Actor-Critic based ABR algorithm for the semantic codec to enhance the robustness of the proposed ABRVSC scheme against network variations. Simulation results demonstrate that at low bitrates, the mean intersection over union (MIoU) of the proposed ABRVSC scheme is nearly twice that of the traditional scheme. Moreover, the proposed ABRVSC scheme, which increases the QoE in video semantic communication by 36.57%, exhibits more robustness against network variations compared to both the fixed bitrate schemes and traditional ABR schemes.

翻译：本文研究了无线网络上的自适应比特率（ABR）视频语义通信。在所考虑的模型中，视频传感设备必须向边缘服务器传输视频语义信息，以支持泛在视频感知服务，例如在自动驾驶场景中边缘服务器上的道路环境监控。然而，由于无线网络条件的变化，如果设备持续传输固定比特率的视频语义信息，则难以同时保证低传输延迟和高语义精度。为解决这一挑战，我们开发了一种自适应比特率视频语义通信（ABRVSC）系统，其中设备根据网络条件自适应调整视频语义信息的比特率。具体而言，首先定义了视频语义通信中的体验质量（QoE）。随后，提出了一种基于Swin Transformer的语义编解码器，在考虑QoE影响的情况下提取语义信息。然后，我们提出了一种基于Actor-Critic的ABR算法用于语义编解码器，以增强所提出的ABRVSC方案对网络变化的鲁棒性。仿真结果表明，在低比特率下，所提ABRVSC方案的平均交并比（MIoU）几乎是传统方案的两倍。此外，所提出的ABRVSC方案将视频语义通信中的QoE提高了36.57%，与固定比特率方案和传统ABR方案相比，对网络变化表现出更强的鲁棒性。