One of the major concerns of IPTV network deployment is channel change delay (also known as zapping delay). This delay can add up to 2 s or more, and its main culprits are synchronisation and buffering of the media streams. Proving the importance of the problem is the already significant amount of literature addressing it. We start this paper with a survey of techniques proposed to reduce IPTV channel change delay. Then, by analysing an extensive dataset from an operational IPTV provider comprising 255 thousand users, 150 TV channels, and covering a 6-month period we have observed that most channel switching events are relatively predictable: users very frequently switch linearly, up or down to the next TV channel. This fact motivated us to use this dataset to analyse in detail a specific type of solutions to this problem, namely, predictive pre-joining of TV channels. In these schemes each set top box (STB) simultaneously joins additional multicast groups (TV channels) along with the one that is requested by the user. If the user switches to any of these channels the switching latency is virtually eliminated, not affecting therefore user's experience. We start by evaluating a simple scheme, where the neighbouring channels (i.e., channels adjacent to the requested one) are pre-joined by the STB during zapping periods. Notwithstanding the simplicity of this scheme, trace-driven simulations show that the zapping delay can be virtually eliminated for a significant percentage of channel switching requests. For example, when sending the previous and the next channel concurrently with the requested one, for only 1 min after a zapping event, switching delay is eliminated for close to half of all channel switching requests. Importantly, this result is achieved with a negligible increase of bandwidth utilisation in the access link. Other more complex schemes where user behaviour is tracked were also evaluated, but the improvement over the simple scheme was insignificant.
- Channel change delay
- Zapping delay
ASJC Scopus subject areas
- Signal Processing
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering