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| Content Provider | ACM Digital Library |
|---|---|
| Author | Crowcroft, Jon Moon, Sue Amatriain, Xavier Rodriguez, Pablo Cha, Meeyoung |
| Abstract | For half a century, television has been a dominant and pervasive mass media, driving many technological advances. Despite its widespread usage and importance to emerging applications, the ingrained TV viewing habits are not completely understood. This was primarily due to the difficulty of instrumenting monitoring devices at individual homes at a large scale. The recent boom of Internet TV (IPTV) has enabled us to monitor the user behavior and network usage of an entire network. Such analysis can provide a clearer picture of how people watch TV and how the underlying networks and systems can better adapt to future challenges. In this paper, we present the first analysis of IPTV workloads based on network traces from one of the world's largest IPTV systems. Our dataset captures the channel change activities of 250,000 households over a six month period. We characterize the properties of viewing sessions, channel popularity dynamics, geographical locality, and channel switching behaviors. We discuss implications of our findings on networks and systems, including the support needed for fast channel changes. Our data analysis of an operational IPTV system has important implications on not only existing and future IPTV systems, but also the design of the open Internet TV distribution systems such as Joost and BBC's iPlayer that distribute television on the wider Internet. |
| Starting Page | 71 |
| Ending Page | 84 |
| Page Count | 14 |
| File Format | |
| ISBN | 9781605583341 |
| DOI | 10.1145/1452520.1452529 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2008-10-20 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Channel switching behavior Network measurement Iptv |
| Content Type | Text |
| Resource Type | Article |
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