Catch-up TV forecasting: enabling next-generation over-the-top multimedia TV services

摘要

Due to recent developments in Over-The-Top (OTT) technologies, Pay-TV operators have begun a migration process of managed IP Television (IPTV) services to more appealing OTT approaches. In these scenarios, being able to predict when and what resources will be necessary at any given point is crucial to a high-quality, efficient, and cost-effective operation, especially when dealing with the dynamic and resource-intensive requirements of IPTV multimedia services. To evaluate the advantages of demand forecasting for efficient Catch-up TV delivery on OTT scenarios, this research work explores several classes of machine learning models regarding their accuracy, computational requirement trade-offs, and deployability. The training process relies on a dataset comprised of Catch-up TV usage logs acquired from an IPTV operator’s live production service containing over 1 million subscribers. A predictive and dynamic resource provisioning approach is proposed and evaluated in terms of bandwidth and storage savings. Results demonstrate that forecasting Catch-up TV demand is practical, suitable for integration in OTT solutions, and useful in improving efficiency, with benefits to operators and consumers. Significant savings in bandwidth and storage are shown to be achievable, enabling green and cost-effective resource usage.