  In this paper, we propose an adaptive panoramic video semantic transmission
(APVST) framework enabled by rate splitting multiple access (RSMA). The APVST
framework consists of a semantic transmitter and receiver, utilizing a deep
joint source-channel coding structure to adaptively extract and encode semantic
features from panoramic frames. To achieve higher spectral efficiency and
conserve bandwidth, APVST employs an entropy model and a dimension-adaptive
module to control the transmission rate. Additionally, we take
weighted-to-spherically-uniform peak signal-to-noise ratio (WS-PSNR) and
weighted-to-spherically-uniform structural similarity (WS-SSIM) as distortion
evaluation metrics for panoramic videos and design a weighted self-attention
module for APVST. This module integrates weights and feature maps to enhance
the quality of the immersive experience. Considering the overlap in the field
of view when users watch panoramic videos, we further utilize RSMA to split the
required panoramic video semantic streams into common and private messages for
transmission. We propose an RSMA-enabled semantic stream transmission scheme
and formulate a joint problem of latency and immersive experience quality by
optimizing the allocation ratios of power, common rate, and channel bandwidth,
aiming to maximize the quality of service (QoS) scores for users. To address
the above problem, we propose a deep reinforcement learning algorithm based on
proximal policy optimization (PPO) with high efficiency to handle dynamically
changing environments. Simulation results demonstrate that our proposed APVST
framework saves up to 20% and 50% of channel bandwidth compared to other
semantic and traditional video transmission schemes, respectively. Moreover,
our study confirms the efficiency of RSMA in panoramic video transmission,
achieving performance gains of 13% and 20% compared to NOMA and OFDMA.
