{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T09:50:30Z","timestamp":1762509030619,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031427848"},{"type":"electronic","value":"9783031427855"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-42785-5_19","type":"book-chapter","created":{"date-parts":[[2023,8,25]],"date-time":"2023-08-25T12:03:37Z","timestamp":1692965017000},"page":"281-295","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Real-Time Data Transmission Optimization on\u00a05G Remote-Controlled Units Using Deep Reinforcement Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6955-830X","authenticated-orcid":false,"given":"Nikita","family":"Smirnov","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5825-8915","authenticated-orcid":false,"given":"Sven","family":"Tomforde","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,26]]},"reference":[{"issue":"20","key":"19_CR1","doi-asserted-by":"publisher","first-page":"10274","DOI":"10.3390\/app122010274","volume":"12","author":"A Biernacki","year":"2022","unstructured":"Biernacki, A.: Improving streaming video with deep learning-based network throughput prediction. Appl. Sci. 12(20), 10274 (2022). https:\/\/doi.org\/10.3390\/app122010274","journal-title":"Appl. Sci."},{"key":"19_CR2","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1109\/TMM.2020.2985631","volume":"23","author":"L Cui","year":"2021","unstructured":"Cui, L., Su, D., Yang, S., Wang, Z., Ming, Z.: TCLiVi: transmission control in live video streaming based on deep reinforcement learning. IEEE Trans. Multimedia 23, 651\u2013663 (2021). https:\/\/doi.org\/10.1109\/TMM.2020.2985631","journal-title":"IEEE Trans. Multimedia"},{"issue":"10","key":"19_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3519552","volume":"54","author":"NN Dao","year":"2022","unstructured":"Dao, N.N., Tran, A.T., Tu, N.H., Thanh, T.T., Bao, V.N.Q., Cho, S.: A contemporary survey on live video streaming from a computation-driven perspective. ACM Comput. Surv. 54(10), 1\u201338 (2022). https:\/\/doi.org\/10.1145\/3519552","journal-title":"ACM Comput. Surv."},{"doi-asserted-by":"crossref","unstructured":"Feng, Y., Liu, S., Zhu, Y.: Real-time spatio-temporal lidar point cloud compression (2020)","key":"19_CR4","DOI":"10.1109\/IROS45743.2020.9341071"},{"unstructured":"Huang, S., Dossa, R.F.J., Raffin, A., Kanervisto, A., Wang, W.: The 37 implementation details of proximal policy optimization (2022). https:\/\/iclr-blog-track.github.io\/2022\/03\/25\/ppo-implementation-details\/. Accessed 08 Aug 2023","key":"19_CR5"},{"doi-asserted-by":"publisher","unstructured":"Jiang, J., Sekar, V., Zhang, H.: Improving fairness, efficiency, and stability in HTTP-based adaptive video streaming with FESTIVE. In: Proceedings of the 8th International Conference on Emerging Networking Experiments and Technologies, pp. 97\u2013108 (2012). https:\/\/doi.org\/10.1145\/2413176.2413189","key":"19_CR6","DOI":"10.1145\/2413176.2413189"},{"doi-asserted-by":"publisher","unstructured":"Kaur, A., Singh, S.: A survey of streaming protocols for video transmission. In: Proceedings of the International Conference on Data Science, Machine Learning and Artificial Intelligence, pp. 186\u2013191. Association for Computing Machinery, New York (2022). https:\/\/doi.org\/10.1145\/3484824.3484892","key":"19_CR7","DOI":"10.1145\/3484824.3484892"},{"unstructured":"Mao, H., Chen, S., Dimmery, D., Singh, S., Blaisdell, D., Tian, Y., et al.: Real-world video adaptation with reinforcement learning (2020)","key":"19_CR8"},{"issue":"3","key":"19_CR9","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1145\/263932.264023","volume":"27","author":"M Mathis","year":"1997","unstructured":"Mathis, M., Semke, J., Mahdavi, J., Ott, T.: The macroscopic behavior of the TCP congestion avoidance algorithm. SIGCOMM Comput. Commun. Rev. 27(3), 67\u201382 (1997). https:\/\/doi.org\/10.1145\/263932.264023","journal-title":"SIGCOMM Comput. Commun. Rev."},{"doi-asserted-by":"crossref","unstructured":"M\u00fcller-Schloer, C., Tomforde, S.: Organic Computing - Technical Systems for Survival in the Real World. Birkh\u00e4user (2017)","key":"19_CR10","DOI":"10.1007\/978-3-319-68477-2"},{"key":"19_CR11","doi-asserted-by":"publisher","first-page":"181176","DOI":"10.1109\/ACCESS.2020.3028550","volume":"8","author":"G Nardini","year":"2020","unstructured":"Nardini, G., Sabella, D., Stea, G., Thakkar, P., Virdis, A.: Simu5G-An OMNeT++ library for end-to-end performance evaluation of 5G networks. IEEE Access 8, 181176\u2013181191 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3028550","journal-title":"IEEE Access"},{"key":"19_CR12","doi-asserted-by":"publisher","first-page":"109565","DOI":"10.1109\/ACCESS.2022.3214827","volume":"10","author":"M Nguyen","year":"2022","unstructured":"Nguyen, M., Lorenzi, D., Tashtarian, F., Hellwagner, H., Timmerer, C.: DoFP+: an HTTP\/3-based adaptive bitrate approach using retransmission techniques. IEEE Access 10, 109565\u2013109579 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3214827","journal-title":"IEEE Access"},{"issue":"268","key":"19_CR13","first-page":"1","volume":"22","author":"A Raffin","year":"2021","unstructured":"Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: reliable reinforcement learning implementations. J. Mach. Learn. Res. 22(268), 1\u20138 (2021)","journal-title":"J. Mach. Learn. Res."},{"issue":"22","key":"19_CR14","doi-asserted-by":"publisher","first-page":"2786","DOI":"10.3390\/electronics10222786","volume":"10","author":"VP Rekkas","year":"2021","unstructured":"Rekkas, V.P., Sotiroudis, S., Sarigiannidis, P., Wan, S., Karagiannidis, G.K., Goudos, S.K.: Machine learning in beyond 5G\/6G networks - state-of-the-art and future trends. Electronics 10(22), 2786 (2021). https:\/\/doi.org\/10.3390\/electronics10222786","journal-title":"Electronics"},{"key":"19_CR15","doi-asserted-by":"publisher","first-page":"10343","DOI":"10.3390\/app122010343","volume":"12","author":"A del R\u00edo Ponce","year":"2022","unstructured":"del R\u00edo Ponce, A., Serrano Romero, J., Jimenez Bermejo, D., Contreras, L., Alvarez, F.: A deep reinforcement learning quality optimization framework for multimedia streaming over 5G networks. Appl. Sci. 12, 10343 (2022). https:\/\/doi.org\/10.3390\/app122010343","journal-title":"Appl. Sci."},{"issue":"9","key":"19_CR16","doi-asserted-by":"publisher","first-page":"208","DOI":"10.3390\/a13090208","volume":"13","author":"GL Santos","year":"2020","unstructured":"Santos, G.L., Endo, P.T., Sadok, D., Kelner, J.: When 5G meets deep learning: a systematic review. Algorithms 13(9), 208 (2020). https:\/\/doi.org\/10.3390\/a13090208","journal-title":"Algorithms"},{"unstructured":"Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms (2017)","key":"19_CR17"},{"issue":"1","key":"19_CR18","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1109\/JETCAS.2018.2885981","volume":"9","author":"S Schwarz","year":"2019","unstructured":"Schwarz, S., Preda, M., Baroncini, V., Budagavi, M., Cesar, P., Chou, P.A., et al.: Emerging MPEG standards for point cloud compression. IEEE J. Emerg. Sel. Top. Circ. Syst. 9(1), 133\u2013148 (2019). https:\/\/doi.org\/10.1109\/JETCAS.2018.2885981","journal-title":"IEEE J. Emerg. Sel. Top. Circ. Syst."},{"doi-asserted-by":"publisher","unstructured":"Smirnov, N., Tomforde, S.: Navigation support for an autonomous ferry using deep reinforcement learning in simulated maritime environments. In: 2022 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA), pp. 142\u2013149 (2022). https:\/\/doi.org\/10.1109\/CogSIMA54611.2022.9830689","key":"19_CR19","DOI":"10.1109\/CogSIMA54611.2022.9830689"},{"issue":"4","key":"19_CR20","doi-asserted-by":"publisher","first-page":"1698","DOI":"10.1109\/TNET.2020.2996964","volume":"28","author":"K Spiteri","year":"2020","unstructured":"Spiteri, K., Urgaonkar, R., Sitaraman, R.K.: BOLA: near-optimal bitrate adaptation for online videos. IEEE\/ACM Trans. Networking 28(4), 1698\u20131711 (2020). https:\/\/doi.org\/10.1109\/TNET.2020.2996964","journal-title":"IEEE\/ACM Trans. Networking"}],"container-title":["Lecture Notes in Computer Science","Architecture of Computing Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-42785-5_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,25]],"date-time":"2023-08-25T12:06:35Z","timestamp":1692965195000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-42785-5_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031427848","9783031427855"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-42785-5_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"26 August 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ARCS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Architecture of Computing Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Athens","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 June 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 June 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"36","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"arcs2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/arcs-conference.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"29","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"21","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"72% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}