{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:09:05Z","timestamp":1772118545268,"version":"3.50.1"},"reference-count":10,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T00:00:00Z","timestamp":1750896000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Internet Technology Letters"],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>In this paper, we propose LM\u2010QoEStream, a novel framework that integrates large\u2010scale language models (LLMs) with reinforcement learning\u2010based streaming scheduling to optimize music delivery under dynamic wireless conditions. Specifically, we design a prompt\u2010driven Quality of Experience (QoE) prediction module that transforms heterogeneous user, content, and network features into structured natural language prompts, enabling the LLM to infer fine\u2010grained user satisfaction scores. These scores are then used as rewards in a Soft Actor\u2010Critic (SAC) reinforcement learning (RL) controller that dynamically adjusts streaming parameters such as bitrate and buffer strategies. Extensive experiments conducted on simulated 5G\/6G networks with real\u2010world content and user interaction traces demonstrate that LM\u2010QoEStream significantly outperforms baseline methods in terms of average QoE, stall ratio, bitrate adaptation accuracy, and fairness. Ablation studies further confirm the complementary strengths of both the LLM\u2010based perception model and the learning\u2010based decision module. The proposed approach offers a scalable, generalizable, and user\u2010centric solution for next\u2010generation music streaming systems.<\/jats:p>","DOI":"10.1002\/itl2.70062","type":"journal-article","created":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T19:10:04Z","timestamp":1750965004000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Intelligent Music Streaming Scheduling and\n                    <scp>QoE<\/scp>\n                    Optimization in\n                    <scp>6G<\/scp>\n                    Wireless Networks Using Large\u2010Scale Models"],"prefix":"10.1002","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-3222-3212","authenticated-orcid":false,"given":"Xudong","family":"Qiao","sequence":"first","affiliation":[{"name":"Henan Polytechnic  Zhengzhou China"}]}],"member":"311","published-online":{"date-parts":[[2025,6,26]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/3229294"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aej.2022.08.017"},{"key":"e_1_2_6_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBC.2023.3345656"},{"key":"e_1_2_6_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-023-00626-4"},{"key":"e_1_2_6_6_1","first-page":"331","volume-title":"Handbooks in Operations Research and Management Science","author":"Puterman M. 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