Rate Splitting Multiple Access-Enabled Adaptive Panoramic Video Semantic Transmission

In immersive communication, delivering real-time, high-resolution 360-degree panoramic videos imposes extremely high demands on network performance. In this paper, we propose a rate splitting multiple access (RSMA)-enabled adaptive panoramic video semantic transmission (APVST) framework. Specificall...

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Published in:IEEE transactions on wireless communications Vol. 24; no. 11; pp. 9050 - 9068
Main Authors: Gao, Haixiao, Sun, Mengying, Xu, Xiaodong, Han, Shujun, Wang, Bizhu, Zhang, Jingxuan, Zhang, Ping
Format: Journal Article
Language:English
Published: New York IEEE 01.11.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Bandwidths
Changing environments
Coding
deep joint source-channel coding (JSCC)
deep reinforcement learning (DRL)
Encoding
Interference
Modules
Multiaccess communication
Multiple access
Network latency
NOMA
Optimization
Panoramic videos
Quality of service
rate splitting multiple access (RSMA)
Real time
Resource management
Semantic communication
Semantics
Splitting
Streaming media
Video transmission
ISSN:1536-1276, 1558-2248
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Summary:In immersive communication, delivering real-time, high-resolution 360-degree panoramic videos imposes extremely high demands on network performance. In this paper, we propose a rate splitting multiple access (RSMA)-enabled adaptive panoramic video semantic transmission (APVST) framework. Specifically, APVST is built based on the deep joint source-channel coding (JSCC) structure and achieves adaptive semantic extraction and variable-length coding of panoramic frames. Additionally, APVST employs an entropy model and a latitude adaptive module to jointly achieve rate control, and utilizes a weight attention module to enhance the panoramic video quality. Given the overlapping field of view (FoV) when users watch panoramic videos, RSMA is integrated into the semantic transmission to further improve system efficiency. Therefore, we introduce an RSMA-enabled semantic stream transmission scheme, and formulate a joint optimization problem for latency and video quality by optimizing power, common rate, and channel bandwidth allocation ratios, aiming to maximize the users' quality of service (QoS). To address this problem, we develop a deep reinforcement learning (DRL) approach based on the proximal policy optimization (PPO) algorithm, which integrates semantic-level FoV information to effectively adapt to dynamically changing environments. Simulation results indicate that our proposed APVST reduces bandwidth consumption by 20% compared to semantic video transmission schemes and 45% compared to traditional ones. Furthermore, our research validates the effectiveness of RSMA in panoramic video semantic transmission, demonstrating QoS improvements of up to 20% compared to other multiple access schemes.
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ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2025.3570465