Abstract: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.
| Comments: | 2026 IEEE 104th Vehicular Technology Conference (VTC2026-Fall), 6-9 September 2026, Boston, Massachusetts, USA |
| Subjects: | Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.16735 [cs.NI] |
| (or arXiv:2605.16735v1 [cs.NI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.16735 arXiv-issued DOI via DataCite (pending registration) |
From: Kasidis Arunruangsirilert [view email]
[v1]
Sat, 16 May 2026 01:10:55 UTC (18,351 KB)
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