  Wireless extended reality (XR) has attracted wide attentions as a promising
technology to improve users' mobility and quality of experience. However, the
ultra-high data rate requirement of wireless XR has hindered its development
for many years. To overcome this challenge, we develop a semantic communication
framework, where semantically-unimportant information is highly-compressed or
discarded in semantic coders, significantly improving the transmission
efficiency. Besides, considering the fact that some source content may have
less amount of semantic information or have higher tolerance to channel noise,
we propose a universal variable-length semantic-channel coding method. In
particular, we first use a rate allocation network to estimate the best code
length for semantic information and then adjust the coding process accordingly.
By adopting some proxy functions, the whole framework is trained in an
end-to-end manner. Numerical results show that our semantic system
significantly outperforms traditional transmission methods and the proposed
variable-length coding scheme is superior to the fixed-length coding methods.
