Adaptive Video Streaming: Navigating Challenges, Embracing Personalization, and Charting Future Frontiers
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 a...
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| Format: | Article |
| Language: | English |
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International Transactions on Electrical Engineering and Computer Science
2023-12-01
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| Series: | International Transactions on Electrical Engineering and Computer Science |
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| Online Access: | https://iteecs.com/index.php/iteecs/article/view/63 |
| _version_ | 1797371908135583744 |
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| author | Koffka Khan |
| author_facet | Koffka Khan |
| author_sort | Koffka Khan |
| collection | DOAJ |
| description |
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.
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| first_indexed | 2024-03-08T18:28:41Z |
| format | Article |
| id | doaj.art-57c3b9e8cde147d8adc88de956bb6228 |
| institution | Directory Open Access Journal |
| issn | 2583-6471 |
| language | English |
| last_indexed | 2024-03-08T18:28:41Z |
| publishDate | 2023-12-01 |
| publisher | International Transactions on Electrical Engineering and Computer Science |
| record_format | Article |
| series | International Transactions on Electrical Engineering and Computer Science |
| spelling | doaj.art-57c3b9e8cde147d8adc88de956bb62282023-12-30T08:15:20ZengInternational Transactions on Electrical Engineering and Computer ScienceInternational Transactions on Electrical Engineering and Computer Science2583-64712023-12-0124Adaptive Video Streaming: Navigating Challenges, Embracing Personalization, and Charting Future FrontiersKoffka Khan0Department of Computing and Information Technology, University of the West Indies, St. Augustine, Trinidad and Tobago. 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. https://iteecs.com/index.php/iteecs/article/view/63Personalized adaptive streamingMachine learningUser profilingContext aware adaptationEthical considerations |
| spellingShingle | Koffka Khan Adaptive Video Streaming: Navigating Challenges, Embracing Personalization, and Charting Future Frontiers International Transactions on Electrical Engineering and Computer Science Personalized adaptive streaming Machine learning User profiling Context aware adaptation Ethical considerations |
| title | Adaptive Video Streaming: Navigating Challenges, Embracing Personalization, and Charting Future Frontiers |
| title_full | Adaptive Video Streaming: Navigating Challenges, Embracing Personalization, and Charting Future Frontiers |
| title_fullStr | Adaptive Video Streaming: Navigating Challenges, Embracing Personalization, and Charting Future Frontiers |
| title_full_unstemmed | Adaptive Video Streaming: Navigating Challenges, Embracing Personalization, and Charting Future Frontiers |
| title_short | Adaptive Video Streaming: Navigating Challenges, Embracing Personalization, and Charting Future Frontiers |
| title_sort | adaptive video streaming navigating challenges embracing personalization and charting future frontiers |
| topic | Personalized adaptive streaming Machine learning User profiling Context aware adaptation Ethical considerations |
| url | https://iteecs.com/index.php/iteecs/article/view/63 |
| work_keys_str_mv | AT koffkakhan adaptivevideostreamingnavigatingchallengesembracingpersonalizationandchartingfuturefrontiers |