Joint content popularity and audience retention-aware live streaming over RSMA edge networks
Ahmed, Fayshal; Nguyen, The Vinh; Tran, Nam-Phuong; Dao, Nhu Ngoc; Cho, Sungrae (2025-05-23)
Avaa tiedosto
Sisältö avataan julkiseksi: 23.05.2027
Elsevier
23.05.2025
Ahmed, F., Nguyen, T.-V., Tran, N.-P., Dao, N.-N., & Cho, S. (2025). Joint content popularity and audience retention-aware live streaming over RSMA edge networks. Computer Networks, 265, 111301. https://doi.org/10.1016/j.comnet.2025.111301
https://creativecommons.org/licenses/by-nc-nd/4.0/
© 2025. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http:/creativecommons.org/licenses/by-nc-nd/4.0/
https://creativecommons.org/licenses/by-nc-nd/4.0/
© 2025. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http:/creativecommons.org/licenses/by-nc-nd/4.0/
https://creativecommons.org/licenses/by-nc-nd/4.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202505023026
https://urn.fi/URN:NBN:fi:oulu-202505023026
Tiivistelmä
Abstract
The exponential growth of high-quality live streaming services over cellular networks, particularly in heterogeneous environments facilitated by 6G, has underscored the need for novel wireless communication. To address this challenge, Rate Splitting Multiple Access (RSMA) has emerged as a promising interference management scheme in advanced cellular networks. This paper considers such a potential environment where the impacts of content popularity and audience retention are jointly investigated to maximize the average video resolution of live streaming services over RSMA edge networks. The complex problem is modeled as a Markov Decision Process and subsequently addressed using an appropriate reinforcement learning framework leveraging the Deep Deterministic Policy Gradient (DDPG) technique, named DDPG-BARMAS. Simulation results demonstrate that the proposed DDPG-BARMAS method significantly outperforms existing algorithms in terms of video resolution improvement, highlighting its potential as a robust solution for future wireless live-streaming services.
The exponential growth of high-quality live streaming services over cellular networks, particularly in heterogeneous environments facilitated by 6G, has underscored the need for novel wireless communication. To address this challenge, Rate Splitting Multiple Access (RSMA) has emerged as a promising interference management scheme in advanced cellular networks. This paper considers such a potential environment where the impacts of content popularity and audience retention are jointly investigated to maximize the average video resolution of live streaming services over RSMA edge networks. The complex problem is modeled as a Markov Decision Process and subsequently addressed using an appropriate reinforcement learning framework leveraging the Deep Deterministic Policy Gradient (DDPG) technique, named DDPG-BARMAS. Simulation results demonstrate that the proposed DDPG-BARMAS method significantly outperforms existing algorithms in terms of video resolution improvement, highlighting its potential as a robust solution for future wireless live-streaming services.
Kokoelmat
- Avoin saatavuus [37689]
Ellei muuten mainita, aineiston lisenssi on https://creativecommons.org/licenses/by-nc-nd/4.0/