Prefetching schemes and performance analysis for TV on demand services

TV-on-Demand services have become one of the most popular Internet applications that continuously attracts high user interest. With rapidly increasing user demands, the existing network conditions may not be able to ensure a low start-up delay of video playback. Prefetching has been broadly investigated to cope with the start-up latency problem, which is also known as user perceived latency. In this paper, two datasets from different IPTV providers are used to analyse the TV program request patterns. According to the results, we propose a prefetching scheme at the user end to preload videos before user requests. For both datasets, our prefetching scheme significantly improves the cache hit ratio compared to passive caching and we note that there is a potential to further improve prefetching performance by customizing prefetching schemes for different video categories. We further present a cost model to determine the optimal number of videos to prefetch. We also discuss if there is enough time for prefetching. Finally, more factors, which may have an impact onoptimizing prefetching performance, are further discussed, such as the jump patterns over different time in a day and the the distribution of each video’s viewing length.