Bandwidth Allocation Issues in Near Video on Demand Services

Introduction

Video on demand (VOD) proposes to offer its subscribers the possibility of watching the program of their choice at the time of their choice, as if they were watching a rented video cassette. Despite the obvious appeal of the concept, VOD has yet to succeed on the marketplace because it has to compete against cheaper alternatives such as pay-per-per view and video cassette rentals.

One way to reduce the cost of VOD is to schedule repeated broadcasts of the videos that are likely to be watched by many viewers rather than waiting for individual requests. This technique is known as video broadcasting. The savings that can be achieved are considerable, as it is often the case that 40 percent of the demand is for a small number of, say, 10 to 20, hot videos. Depending on the frequency at which these videos are rebroadcast, customers may have to wait between a few minutes to, say, half an hour before watching the video of their choice. Hence this type of service can either be referred to as near video on demand (NVOD) or enhanced pay per view (EPPV).

One of the most important decisions that have to be made by the provider of a NVOD service is the time interval at which each video should be rebroadcast. Very frequent rebroadcasts will minimize the average customer waiting time but also increase cost of the service. Less frequent rebroadcasts may, on the other hand, result in a significant loss of revenue if too many customers decide not to wait for the next time the video will be rebroadcast.

This paper addresses the problem of selecting the optimal rebroadcasting frequency for each video. The solution we propose consists of selecting the rebroadcasting frequencies that maximize the expected number of viewers per unit of bandwidth. To evaluate this expected number of viewers, we will represent the customer demand by (a) the rate at which any video would be ordered if it was available for immediate viewing and (b) a tolerance function expressing the customer willingness to wait for a given amount of time before watching the video of their choice. We will present two strategies tailored to two specific tolerance functions; namely, a step function and a three-level staircase function. As it may be expected, the outcome of these strategies will strongly depend on the relative popularity of the videos being broadcast.