Adaptive Video Streaming: Navigating Challenges, Embracing Personalization, and Charting Future Frontiers

Authors

  • Koffka Khan Department of Computing and Information Technology, University of the West Indies, St. Augustine, Trinidad and Tobago.

DOI:

https://doi.org/10.62760/iteecs.2.4.2023.63

Keywords:

Personalized adaptive streaming, Machine learning, User profiling, Context aware adaptation, Ethical considerations

Abstract

This review paper explores the paradigm of personalized adaptive streaming, where machine learning techniques are employed to tailor video streaming experiences based on individual user behavior, preferences, and contextual factors. The paper begins by elucidating the evolution of video streaming and the critical role of adaptive streaming in modern multimedia consumption. It provides a comprehensive overview of adaptive video streaming, covering its basics, traditional approaches, and associated challenges. Emphasizing the significance of personalization in enhancing user experience, the paper then delves into the integration of machine learning in adaptive streaming systems. Specific personalized adaptive streaming techniques, including user profiling, context-aware adaptation, and real-time adjustments based on user behavior, are discussed in detail. Case studies and applications showcase notable platforms, successes, and challenges. A comparative analysis of machine learning models and algorithms is conducted, followed by a discussion on current challenges, ethical considerations and future research directions. The paper concludes by summarizing key findings and urging researchers and industry practitioners to contribute to the evolving landscape of personalized adaptive streaming.

References

J. Aguilar-Armijo, C. Timmerer and H. Hellwagner “SPACE: Segment Prefetching and Caching at the Edge for Adaptive Video Streaming”, IEEE Access, Vol. 11, pp. 21783-21798, 2023. https://doi.org/10.1109/ACCESS.2023.3252365

A. Bentaleb, Z. Zhan, F. Tashtarian, M. Lim, S. Harous, C. Timmerer, H. Hellwagner, R. Zimmermann “Low Latency Live Streaming Implementation in DASH and HLS”, Proceedings of the 30th ACM International Conference on Multimedia, pp. 7343-7346, 2022. https://doi.org/10.1145/3503161.3548544

T. Evens, A. Henderickx, P. Conradie “Technological affordances of video streaming platforms: Why people prefer video streaming platforms over television”, European Journal of Communication, pp. 1-19, 2023. https://doi.org/10.1177/02673231231155731

M. Ghaznavi, E. Jalalpour, M. A. Salahuddin, R. Boutaba, D. Migault, S. Preda “Content delivery network security: A survey”, IEEE Communications Surveys & Tutorials, Vol. 23, No. 4, pp. 2166-90, 2021. https://doi.org/10.1109/COMST.2021.3093492

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

M. Khan, S. Khusro “Towards the design of personalized adaptive user interfaces for smart TV viewers”, Journal of King Saud University-Computer and Information Sciences, Vol. 35, No.9, article. 101777, 2023. https://doi.org/10.1016/j.jksuci.2023.101777

K. Koffka and A. Sahai “A comparison of BA, GA, PSO, BP and LM for training feed forward neural networks in e-learning context”, International Journal of Intelligent Systems and Applications, Vol. 4, No. 7, pp. 23-29, 2012. https://doi.org/10.5815/ijisa.2012.07.03

K. Koffka and W. Goodridge “QoE evaluation of dynamic adaptive streaming over HTTP (DASH) with promising transport layer protocols: Transport layer protocol performance over HTTP/2 DASH”, CCF Transactions on Networking, Vol. 3, No. 3-4, pp. 245-260, 2020.

K. Koffka and W. Goodridge “QoE Evaluation of Legacy TCP Variants over DASH”, International Journal of Advanced Networking and Applications, Vol. 12, No. 5, pp. 4656-4667, 2021.

K. Koffka and W. Goodridge “Reinforcement Learning in DASH”, International Journal of Advanced Networking and Applications, Vol. 11, No. 5, pp. 4386-4392, 2020.

K. Koffka and W. Goodridge “SAND and Cloud-based Strategies for Adaptive Video Streaming”, International Journal of Advanced Networking and Applications, Vol. 9, No. 3, pp. 3400-3410, 2017.

M. Kim, K. Chung “HTTP adaptive streaming scheme based on reinforcement learning with edge computing assistance”, Journal of Network and Computer Applications, Vol. 213, article. 103604, 2023. https://doi.org/10.1016/j.jnca.2023.103604

K. Khan and W. Goodridge “A DASH Survey: the ON-OFF Traffic Problem and Contemporary Solutions”, Computer Sciences and Telecommunications, Vol. 1, pp. 3-20, 2018.

C. R. Oehrn, S. Cernera, L. H. Hammer, M. Shcherbakova, J. Yao, A. Hahn, S. Wang, J. L. Ostrem, S. Little, P. A. Starr “Personalized chronic adaptive deep brain stimulation outperforms conventional stimulation in Parkinson’s disease”, medRxiv, pp. 1-45, 2023.

A. Ooka, Y. Hayamizu and H. Asaeda “HLS and CCNx Based High-Quality Live Streaming on On-Premises Network System”, 2022 IEEE International Conference on Communications Workshops (ICC Workshops), Seoul, Korea, Republic of, pp. 1-6, 2022. https://doi.org/10.1109/ICCWorkshops53468.2022.9882155

V. R. Ortega “We Pay to Buy Ourselves’: Netflix, Spectators & Streaming”, Journal of Communication Inquiry, Vol. 47, No. 2, pp. 126-44, 2022. https://doi.org/10.1177/01968599211072446

W. E. Shabrina, D. Wisaksono Sudiharto, E. Ariyanto and M. A. Makky “The QoS Improvement Using CDN for Live Video Streaming with HLS”, 2020 International Conference on Smart Technology and Applications (ICoSTA), Surabaya, Indonesia, pp. 1-5, 2020. https://doi.org/10.1109/ICoSTA48221.2020.1570613984

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

A. L. Suárez-Cetrulo, D. Quintana, A. Cervantes “A survey on machine learning for recurring concept drifting data streams”, Expert Systems with Applications, Vol. 213, article. 118934, 2023. https://doi.org/10.1016/j.eswa.2022.118934

J. V. Der Hooft, T. Wauters, F. D. Turck, C. Timmerer, H. Hellwagner “Towards 6dof http adaptive streaming through point cloud compression”, Proceedings of the 27th ACM International Conference on Multimedia 2019, pp. 2405-2413, 2019. https://doi.org/10.1145/3343031.3350917

J. Zhao, P. Liang, W. Liufu, Z. Fan “Recent developments in content delivery network: A survey”, Parallel Architectures, Algorithms and Programming: 10th International Symposium, PAAP 2019, Guangzhou, China, pp. 98-106, 2019. https://doi.org/10.1007/978-981-15-2767-8_9

B. Zolfaghari, G. Srivastava, S. Roy, H. R. Nemati, F. Afghah, T. Koshiba, A. Razi, K. Bibak, P. Mitra, B. K. Rai “Content delivery networks: State of the art, trends, and future roadmap” ACM Computing Surveys, Vol. 53, No. 2, pp.1-34, 2020. https://doi.org/10.1145/3380613

Additional Files

Published

2023-12-30

How to Cite

Khan, K. (2023). Adaptive Video Streaming: Navigating Challenges, Embracing Personalization, and Charting Future Frontiers. International Transactions on Electrical Engineering and Computer Science, 2(4), 172–182. https://doi.org/10.62760/iteecs.2.4.2023.63

Issue

Vol. 2 No. 4 (2023): December

Section

Articles

License

Copyright (c) 2023 Koffka Khan

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.