YTLive: A Dataset of Real-World YouTube Live Streaming Sessions
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912675230908416 |
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| author | Mozhganfar, Mojtaba Jamshidi, Pooya Aghamiri, Seyyed Ali Ghasemi, Mohsen Dolati, Mahdi Tashtarian, Farzad Khonsari, Ahmad Timmerer, Christian |
| author_facet | Mozhganfar, Mojtaba Jamshidi, Pooya Aghamiri, Seyyed Ali Ghasemi, Mohsen Dolati, Mahdi Tashtarian, Farzad Khonsari, Ahmad Timmerer, Christian |
| contents | Live streaming plays a major role in today's digital platforms, supporting entertainment, education, social media, etc. However, research in this field is limited by the lack of large, publicly available datasets that capture real-time viewer behavior at scale. To address this gap, we introduce YTLive, a public dataset focused on YouTube Live. Collected through the YouTube Researcher Program over May and June 2024, YTLive includes more than 507000 records from 12156 live streams, tracking concurrent viewer counts at five-minute intervals along with precise broadcast durations. We describe the dataset design and collection process and present an initial analysis of temporal viewing patterns. Results show that viewer counts are higher and more stable on weekends, especially during afternoon hours. Shorter streams attract larger and more consistent audiences, while longer streams tend to grow slowly and exhibit greater variability. These insights have direct implications for adaptive streaming, resource allocation, and Quality of Experience (QoE) modeling. YTLive offers a timely, open resource to support reproducible research and system-level innovation in live streaming. The dataset is publicly available at github. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_24769 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | YTLive: A Dataset of Real-World YouTube Live Streaming Sessions Mozhganfar, Mojtaba Jamshidi, Pooya Aghamiri, Seyyed Ali Ghasemi, Mohsen Dolati, Mahdi Tashtarian, Farzad Khonsari, Ahmad Timmerer, Christian Multimedia Social and Information Networks Live streaming plays a major role in today's digital platforms, supporting entertainment, education, social media, etc. However, research in this field is limited by the lack of large, publicly available datasets that capture real-time viewer behavior at scale. To address this gap, we introduce YTLive, a public dataset focused on YouTube Live. Collected through the YouTube Researcher Program over May and June 2024, YTLive includes more than 507000 records from 12156 live streams, tracking concurrent viewer counts at five-minute intervals along with precise broadcast durations. We describe the dataset design and collection process and present an initial analysis of temporal viewing patterns. Results show that viewer counts are higher and more stable on weekends, especially during afternoon hours. Shorter streams attract larger and more consistent audiences, while longer streams tend to grow slowly and exhibit greater variability. These insights have direct implications for adaptive streaming, resource allocation, and Quality of Experience (QoE) modeling. YTLive offers a timely, open resource to support reproducible research and system-level innovation in live streaming. The dataset is publicly available at github. |
| title | YTLive: A Dataset of Real-World YouTube Live Streaming Sessions |
| topic | Multimedia Social and Information Networks |
| url | https://arxiv.org/abs/2510.24769 |