Enhancing the Quality of Service in Video Game Live Streaming Using Big Data Analytics with DNN Classification and BERT-Based Sentiment Analysis
Received: 14 April 2025 | Revised: 28 April 2025 | Accepted: 10 May 2025 | Online: 24 July 2025
Corresponding author: Sasikumar Perumal
Abstract
As live streaming video gaming platforms become more and more popular, Quality of Service (QoS) must be improved to increase viewer engagement and satisfaction. Many factors affect the user experience, including interactivity, streamer behavior, video quality, and active community engagement. Real-time QoS optimization is still difficult considering changing network conditions, audience participation, and content quality. This study explores the behavior and expectations of participants in live streaming video games using big data analytics. Driven by network stability, stream quality, and audience interaction parameters, a Deep Neural Network (DNN) can predict viewer satisfaction and turnover rates. BERT-based sentiment analysis is used to extract trends of audience engagement and general sentiment from chat interactions and comments. The proposed framework can extract important insights that can guide modifications to improve QoS. Experimental evaluations show that the proposed DNN using BERT improves the accuracy of satisfaction prediction using datasets from Twitch, Facebook Gaming, and YouTube Gaming. In response to real-time QoS demands, the model can dynamically alter bitrate and resolution to optimize streaming performance. The results show that the proposed model can provide better viewer retention, reduce buffering problems, and increase engagement to help content creators and platform developers.
Keywords:
video game live streaming, quality of service, deep neural networks, BERT sentiment analysis, big data analyticsDownloads
References
A. A. Barakabitze et al., "QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey," IEEE Communications Surveys & Tutorials, vol. 22, no. 1, pp. 526–565, 2020. DOI: https://doi.org/10.1109/COMST.2019.2958784
L. J. Cabeza-Ramírez, F. J. Fuentes-García, and G. A. Muñoz-Fernandez, "Exploring the Emerging Domain of Research on Video Game Live Streaming in Web of Science: State of the Art, Changes and Trends," International Journal of Environmental Research and Public Health, vol. 18, no. 6, Jan. 2021, Art. no. 2917. DOI: https://doi.org/10.3390/ijerph18062917
G. G. Hallur, S. Prabhu, and A. Aslekar, "Entertainment in Era of AI, Big Data & IoT," in Digital Entertainment: The Next Evolution in Service Sector, S. Das and S. Gochhait, Eds. Springer Nature, 2021, pp. 87–109. DOI: https://doi.org/10.1007/978-981-15-9724-4_5
N. Yuvaraj, K. Rajput, K. Suganyadevi, M. Aeri, R. P. Shukla, and H. Gurjar, "Multi-Scale Object Detection and Classification using Machine Learning and Image Processing," in 2024 Second International Conference on Data Science and Information System (ICDSIS), May 2024, pp. 1–6. DOI: https://doi.org/10.1109/ICDSIS61070.2024.10594673
A. S. Mohammed, V. Mallikarjunaradhya, M. D. Sreeramulu, N. Boddapati, N. Jiwani, and Y. Natarajan, "Optimizing Real-time Task Scheduling in Cloud-based AI Systems using Genetic Algorithms," in 2024 7th International Conference on Contemporary Computing and Informatics (IC3I), Greater Noida, India, Sep. 2024, pp. 1649–1653. DOI: https://doi.org/10.1109/IC3I61595.2024.10829055
V. Mallikarjunaradhya, M. D. Sreeramulu, A. S. Mohammed, N. Boddapati, K. Gupta, and Y. Natarajan, "Efficient Resource Management for Real-time AI Systems in the Cloud using Reinforcement Learning," in 2024 7th International Conference on Contemporary Computing and Informatics (IC3I), Sep. 2024, pp. 1654–1659. DOI: https://doi.org/10.1109/IC3I61595.2024.10828656
E. Masciari, A. Umair, and M. H. Ullah, "A Systematic Literature Review on AI-Based Recommendation Systems and Their Ethical Considerations," IEEE Access, vol. 12, pp. 121223–121241, 2024. DOI: https://doi.org/10.1109/ACCESS.2024.3451054
G. Mu, L. Dai, X. Li, X. Ju, Y. Chen, and J. Dai, "DIBTBL: A Novel Text Sentiment Analysis Model Based on an Improved Swarm Intelligence Algorithm and Deep Learning," IEEE Access, vol. 12, pp. 158669–158684, 2024. DOI: https://doi.org/10.1109/ACCESS.2024.3487752
N. S. Mullah, W. M. N. W. Zainon, and M. N. Ab Wahab, "Transfer learning approach for identifying negative sentiment in tweets directed to football players," Engineering Applications of Artificial Intelligence, vol. 133, Jul. 2024, Art. no. 108377. DOI: https://doi.org/10.1016/j.engappai.2024.108377
G. Mu, C. Chen, X. Li, J. Li, X. Ju, and J. Dai, "Multimodal Sentiment Analysis of Government Information Comments Based on Contrastive Learning and Cross-Attention Fusion Networks," IEEE Access, vol. 12, pp. 165525–165538, 2024. DOI: https://doi.org/10.1109/ACCESS.2024.3493933
J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding," in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Minneapolis, MN, USA, Mar. 2019, pp. 4171–4186.
S. S. Krishnan and R. K. Sitaraman, "Understanding the effectiveness of video ads: a measurement study," in Proceedings of the 2013 conference on Internet measurement conference, Jul. 2013, pp. 149–162. DOI: https://doi.org/10.1145/2504730.2504748
M. K. Nazir, C. N. Faisal, M. A. Habib, and H. Ahmad, "Leveraging Multilingual Transformer for Multiclass Sentiment Analysis in Code-Mixed Data of Low-Resource Languages," IEEE Access, vol. 13, pp. 7538–7554, 2025. DOI: https://doi.org/10.1109/ACCESS.2025.3527710
M. Dobrojevic et al., "Cyberbullying Sexism Harassment Identification by Metaheurustics-Tuned eXtreme Gradient Boosting," Computers, Materials & Continua, vol. 80, no. 3, pp. 4997–5027, 2024. DOI: https://doi.org/10.32604/cmc.2024.054459
"Twitch Statistics & Charts," TwitchTracker. https://twitchtracker.com/statistics.
C. T. E. R. Hewage and M. G. Martini, "Quality of experience for 3D video streaming," IEEE Communications Magazine, vol. 51, no. 5, pp. 101–107, May 2013. DOI: https://doi.org/10.1109/MCOM.2013.6515053
R. K. Vasudevamurthy and G. T. Raju, "Distributed Streaming Storage Performance Benchmarking: Pravega and Pulsar," Engineering, Technology & Applied Science Research, vol. 14, no. 5, pp. 16242–16251, Oct. 2024. DOI: https://doi.org/10.48084/etasr.8076
Downloads
How to Cite
License
Copyright (c) 2025 Sasikumar Perumal, Kalaivani Kathirvelu
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
