Enhancing Adaptive Video Streaming Through AI-Driven Predictive Analytics for Network Conditions: A Comprehensive Review
DOI:
https://doi.org/10.62760/iteecs.3.1.2024.67Keywords:
Adaptive Video Streaming, Predictive Analytics, AI-driven Decision-making, Network Conditions, Bitrate AdaptationAbstract
As the demand for high-quality video streaming continues to surge, the adaptability of streaming systems to dynamic and unpredictable network conditions becomes paramount. This review paper delves into the realm of adaptive video streaming, focusing on the integration of AI-driven predictive analytics to anticipate and optimize network conditions. The paper provides an extensive overview of existing adaptive streaming algorithms, highlighting the challenges posed by fluctuating network conditions. It explores the role of predictive analytics in mitigating these challenges, emphasizing the use of machine learning models and AI technologies. Through case studies and discussions on real-world implementations, the paper showcases how predictive analytics enhances the decision-making process in adaptive streaming systems, leading to improved bitrate adaptation and content delivery. Challenges and limitations associated with predictive analytics are scrutinized, paving the way for a comprehensive understanding of its implications. The integration of predictive analytics into adaptive streaming systems is examined, emphasizing its potential to revolutionize the quality of service. Finally, the paper outlines future trends and research directions, offering insights into the evolving landscape of adaptive video streaming. This review consolidates knowledge and provides a valuable resource for researchers, practitioners, and industry professionals involved in the intersection of video streaming, predictive analytics, and artificial intelligence.
References
J. P. Bharadiya “A Comparative Study of Business Intelligence and Artificial Intelligence with Big Data Analytics”, American Journal of Artificial Intelligence, Vol. 7, No. 1, pp. 24-30, 2023. https://doi.org/10.11648/j.ajai.20230701.14
P. B. Chanda, S. Das, S. Banerjee, C. Chakraborty “Study on edge computing using machine learning approaches in IoT framework”, InGreen computing and predictive analytics for healthcare, pp. 159-182, 2020.
A. Hasanov, L. H. Laine, T. S. Chung “A survey of adaptive context-aware learning environments”, Journal of Ambient Intelligence and Smart Environments, Vol. 11, No. 5, pp. 403-428, 2019. https://doi.org/10.3233/AIS-190534
Y. Himeur, M. Elnour, F. Fadli, N. Meskin, I. Petri, Y. Rezgui, F. Bensaali, A. Amira “AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives”, Artificial Intelligence Review, Vol. 56, No. 6, pp. 4929-5021, 2023. https://doi.org/10.1007/s10462-022-10286-2
V. C. Paola, J. M. Pulido and M. T. Betancur “A systematic mapping review of context-aware analysis and its approach to mobile learning and ubiquitous learning processes”, Computer Science Review, Vol. 39, art.no. 100335, 2021. https://doi.org/10.1016/j.cosrev.2020.100335
S. Kesavan, E. S. Kumar, A. Kumar, K. Vengatesan “An investigation on adaptive HTTP media streaming Quality-of-Experience (QoE) and agility using cloud media services”, International Journal of Computers and Applications, Vol. 43, No. 5, pp. 431-44, 2021. https://doi.org/10.1080/1206212X.2019.1575034
D. Ghosh, M. Pandey, C. Gautam, A. Vidyarthi, R. Sharma and D. Draheim “Utilizing Continuous Time Markov Chain for analyzing video-on-demand streaming in multimedia systems”, Expert Systems with Applications, Vol. 223, art.no. 119857, 2023. https://doi.org/10.1016/j.eswa.2023.119857
R. Doreswamy, A. G. Colaco, V. Sevani, P. Patil and H. Tyagi, “Rate Adaptation For Low Latency Real-Time Video Streaming," 2023 National Conference on Communications (NCC), Guwahati, India, pp. 1-6, 2023. doi: 10.1109/NCC56989.2023.10067883
L. Fatima, A. B. Letaifa, and T. Aguili “QoE?aware traffic monitoring based on user behavior in video streaming services”, Concurrency and Computation: Practice and Experience, Vol. 35, No. 11, art. no. e6678, 2023. https://doi.org/10.1002/cpe.6678
G. Hakan, O. Ercetin, G. Kalem, and S. Ergut. “QoE evaluation in adaptive streaming: enhanced MDT with deep learning”, Journal of Network and Systems Management, Vol. 31, No. 2, art. no. 41, 2023. https://doi.org/10.1007/s10922-023-09730-7
L. Eirini, D. Xenakis, V. Georgara, G. Kourouniotis, and L. Merakos “Cache-Enabled Adaptive Video Streaming: A QoE-Based Evaluation Study”, Future Internet, Vol. 15, No. 7, art. no. 221, 2023. https://doi.org/10.3390/fi15070221
J. Li et al., “Toward Optimal Real-Time Volumetric Video Streaming: A Rolling Optimization and Deep Reinforcement Learning Based Approach”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 33, No. 12, pp. 7870-7883, 2023. https://doi.org/10.1109/TCSVT.2023.3277893
K. Koffka “A Framework for Meta-Learning in Dynamic Adaptive Streaming over HTTP”, International Journal of Computing, Vol. 12, No. 2, pp. 1-9, 2023. https://doi.org/10.30534/ijccn/2023/021222023
M. Khani, G. Ananthanarayanan, K. Hsieh, J. Jiang, R. Netravali, Y. Shu, M. Alizadeh, V. Bahl RECL: Responsive Resource Efficient Continuous Learning for Video Analytics”, In 20th USENIX Symposium on Networked Systems Design and Implementation, pp. 917-932, 2023.
D. Lindlbauer, A. M. Feit, O. Hilliges “Context-aware online adaptation of mixed reality interfaces”, Proceedings of the 32nd annual ACM symposium on user interface software and technology, pp. 147-160, 2019. https://doi.org/10.1145/3332165.3347945
H. Luo, H. Cai, H. Yu, Y. Sun, Z. Bi, L. Jiang “A short-term energy prediction system based on edge computing for smart city”, Future Generation Computer Systems, Vol. 101, pp. 444-57, 2019. https://doi.org/10.1016/j.future.2019.06.030
A. Majeed, S. O. Hwang “Data-driven analytics leveraging artificial intelligence in the era of COVID-19: an insightful review of recent developments”, Symmetry, Vol. 14, No. 1, art.no. 16, 2021. https://doi.org/10.3390/sym14010016
D. Mourtzis, J. Angelopoulos, N. Panopoulos “Design and Development of an Edge-Computing Platform Towards 5G Technology Adoption for Improving Equipment Predictive Maintenance”, Procedia Computer Science, Vol. 200, pp. 611-619, 2022. https://doi.org/10.1016/j.procs.2022.01.259
E. Ramsahai, N. Dookeram, D. Ramsook, J. R. Rameshwar, A. B. Yearwood, A. Bachoo, K. Khan “Crime prediction in Trinidad and Tobago using big data analytics: Predictive policing in developing countries”, International Journal of Data Science and Analytics, Vol. 15, no. 4, pp. 421-432, 2023.
I. Sarker, A. Colman, J. Han, P. Watters “Context-aware machine learning and mobile data analytics: automated rule-based services with intelligent decision-making”, Springer, 2021. https://doi.org/10.1007/978-3-030-88530-4
W. Shi, G. Pallis, W. Xu “Edge computing [scanning the issue]”, Proceedings of the IEEE, Vol. 107, No. 8, pp. 1474-1481, 2019. https://doi.org/10.1109/JPROC.2019.2928287
K. Spiteri, R. Sitaraman, D. Sparacio “From theory to practice: Improving bitrate adaptation in the DASH reference player”, ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Vol. 15, No. 2s, pp. 1-29, 2019. https://doi.org/10.1145/3336497
J. V. D. Hooft, T. Wauters, F. D. Turck, C. Timmerer, H. Hellwagner “Towards 6dof http adaptive streaming through point cloud compression”, InProceedings of the 27th ACM International Conference on Multimedia, pp. 2405-2413, 2019. https://doi.org/10.1145/3343031.3350917
S. Vasavi, K. Aswarth, T. S. Pavan, A. A. Gokhale “Predictive analytics as a service for vehicle health monitoring using edge computing and AK-NN algorithm”, Materials Today: Proceedings, Vol. 46, pp. 8645-54, 2021. https://doi.org/10.1016/j.matpr.2021.03.658
J. Yang, W. An, C. Yan, P. Zhao, J. Huang “Context-aware domain adaptation in semantic segmentation”, Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 514-524, 2021. https://doi.org/10.48550/arXiv.2003.04010
S. Zulaikha, H. Mohamed, M. Kurniawati, S. Rusgianto, S. A. Rusmita “Customer predictive analytics using artificial intelligence”, The Singapore Economic Review”, Vol. 6, pp. 1-2, 2020. https://doi.org/10.1142/S0217590820480021
Additional Files
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Koffa Khan
This work is licensed under a Creative Commons Attribution 4.0 International License.
This Journal and its metadata are licenced under a 