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Catch-up TV analytics: statistical characterization and consumption patterns identification on a production service

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Abstract

Multimedia IP Television services, such as on-demand Catch-up TV, are in an active migration process towards Over-The-Top (OTT) delivery using state-of-the-art Content Delivery Networks (CDNs). Maintaining the same Quality-of-Experience (QoE) of managed IPTV networks is challenging and requires a thorough understanding of users’ behaviors and content demand characteristics. This article leverages Catch-up TV usage logs obtained from a Pay-TV operator’s live production IPTV service containing over 1 million subscribers to characterize and extract insights from service utilization at a scale and scope not yet addressed in the literature. A detailed analysis on the characteristics of users’ viewings is performed, with a study of when, where, and how often users access the service, along with how they behave during each viewing session. The results show that Catch-up TV consumption exhibits very high levels of utilization throughout the day, and is heavily polarized towards specific genres, recently aired programs, and content broadcasted during prime-time. The superstar effect is notorious. This analysis is complemented by a service optimization perspective, which shows that large gains are achievable by caching popular programs, and by loading content in advance to users’ Set-Top-Boxes (STBs). This comprehensive research study is supplemented by detailed statistical information tables, which highlight the feasibility of efficiently migrating Catch-up TV services to OTT-scenarios, and provide the foundations for future works able to explore these results.

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Acknowledgments

The authors would like to thank Fausto Carvalho (Altice Labs, SA) and João Ferreira (MEO - Serviços de Comunicações e Multimédia, SA) for the key discussions and for providing the raw Catch-up TV consumption dataset.

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Authors and Affiliations

Authors

Corresponding author

Correspondence to João Nogueira.

Additional information

Communicated by A. U. Mauthe.

This research was funded by UltraTV (Portugal 2020 POCI-01-0247-FEDER-017738) and GAPOTT projects (IAPMEI, QREN and COMPETE 2013/34009).

Appendix: Statistical summary tables

Appendix: Statistical summary tables

See Tables 1, 2, 3, 4, 5.

Table 1 Program durations distribution summary statistics (minutes)
Table 2 Per-genre summary statistics
Table 3 Programs available vs. requested summary Statistics
Table 4 Viewing sessions and service usage summary statistics
Table 5 Programs requests delay by genre summary statistics

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Nogueira, J., Guardalben, L., Cardoso, B. et al. Catch-up TV analytics: statistical characterization and consumption patterns identification on a production service. Multimedia Systems 23, 563–581 (2017). https://doi.org/10.1007/s00530-016-0516-7

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