The deployment of 5G Multicast-Broadcast Services (MBS) is emerging as a critical technology for spectral-efficient UHD content delivery and serving as a promising solution to modernize CATV deployment. However, unlike unicast networks that rely on RLC-AM with HARQ retransmissions, MBS broadcast operates in RLC Unacknowledged Mode (RLC-UM), where the absence of a feedback loop means packet loss is permanent and immediately impacts user QoE. Conventional link adaptation algorithms, designed for unicast, typically aggressively maximize throughput and fail in this risk-intolerant environment, resulting in severe video stalls and rebuffering. To address this, we propose a lightweight Transformer-based framework that predicts the success probability of all 28 MCS indices over an upcoming video segment horizon. Utilizing a unique commercial network dataset with 0.5 ms slot-level granularity, we train our model using a custom Asymmetric Safety Loss function that penalizes channel overestimation to prioritize link stability. Experimental results show that our approach achieves a reliability score of 86.89%, significantly outperforming standard AI baselines optimized for raw throughput (31.65%) while maintaining a safe conservative bias. Furthermore, the model is optimized for real-time applications, demonstrating an inference time of less than 0.07 ms on COTS 5G-era smartphones.

翻译：5G多播广播服务（MBS）的部署正成为实现高频谱效率超高清内容传输的关键技术，并有望成为CATV网络现代化改造的解决方案。然而，与依赖RLC-AM模式及HARQ重传的单播网络不同，MBS广播采用RLC非确认模式（RLC-UM），由于缺乏反馈回路，数据包丢失是永久性的，会直接影响用户体验质量（QoE）。传统为单播设计的链路自适应算法通常激进地追求最大化吞吐量，在这种风险不容忍的环境中失效，导致严重视频卡顿和缓冲事件。为解决该问题，我们提出一种轻量级基于Transformer的框架，用于预测未来视频片段周期内全部28个MCS索引的成功概率。利用独特的0.5毫秒时隙粒度商业网络数据集，我们通过自定义非对称安全损失函数训练模型，该函数对信道高估进行惩罚以优先保证链路稳定性。实验结果表明，该方法达到86.89%的可靠性评分，显著优于以原始吞吐量优化为标准的人工智能基线模型（31.65%），同时保持了安全的保守性偏差。此外，该模型针对实时应用进行优化，在商用5G智能手机上推理时间低于0.07毫秒。