<![CDATA[The rapid evolution of Extended Reality (XR) broadcasting over sixth-generation (6G) networks introduces significant challenges related to ultra-low latency, heterogeneous traffic, intensive computation, and dynamic Quality of Experience (QoE) requirements. As a Background, conventional resource allocation and edge computing approaches often process immersive content uniformly, resulting in inefficient bandwidth and computational utilization while failing to prioritize semantically important XR information. The Object of this study is to develop a QoE-aware semantic computing and edge intelligence framework that enables adaptive XR broadcasting by prioritizing perceptually and contextually important content according to network conditions and user experience requirements. The Method integrates semantic feature extraction, intelligent edge processing, QoE-aware resource allocation, and adaptive transmission within a simulated 6G edge network environment. The proposed framework is evaluated under different traffic loads and network conditions using end-to-end latency, bandwidth consumption, computational efficiency, QoE score, and transmission reliability as the primary performance indicators and is compared with a conventional resource allocation approach. The Result demonstrates that the proposed approach reduces transmission latency and unnecessary bandwidth consumption while maintaining higher QoE stability, particularly under congested and highly dynamic network conditions. Semantic prioritization further enables edge resources to focus on XR components with greater perceptual importance without significantly degrading overall immersive quality. The Conclusion confirms that combining semantic computing with edge intelligence provides an effective mechanism for adaptive XR broadcasting and offers a scalable foundation for delivering responsive, resource-efficient, and QoE-oriented immersive media services over future 6G networks.]]>
