Abstract: Estimating ad inventory in an online video streaming system accurately handles interdependencies among targeted attributes. The estimating includes determining a probability distribution of samples taken from electronic data records of ad impressions in an interactive online video streaming service, among a population comprising each different combination of targeted attributes recorded for each of the samples. In addition, the estimating may include populating an electronic data structure with data relating the each different combination of the targeted attributes to corresponding forecasted number of available impressions in a defined time period, based on the probability distribution and a forecasted total number of available impressions in the time period.

Abstract: Estimating a number of unique ad impressions in a streaming video system includes defining parameters of an ad campaign and a desired number of ad impressions for the campaign. A computer system determines a discrete probability distribution of video advertising segments per unit time per client device in a population of video advertising segments streamed to a plurality of client devices, based on historical data. The system randomly samples the probability distribution without replacement, based on the defined number of desired ad impressions. An enhanced binary search algorithm may be used for the sampling. Each sample of the probability distribution identifies a number of ads streamed to a different client device in the probability distribution. The system determines, based on the sampling, a number of unique client devices included the samples, thus obtaining an estimate of unique ad impressions for the defined ad campaign.

Abstract: In a streaming video system with included video ad breaks, a pacing component allocates ads to particular ad breaks so as to pace the distribution of ads over a defined ad campaign period according to a pacing protocol using discrete time bins. A curve fitting algorithm may be used to provide increased or adjustable granularity of time bins used in the pacing protocol.

Abstract: In a streaming video system with included video ad breaks, a pacing component allocates ads to particular ad breaks so as to pace the distribution of ads over a defined ad campaign period according to a pacing protocol using discrete time bins. A curve fitting algorithm may be used to provide increased or adjustable granularity of time bins used in the pacing protocol.

Abstract: Estimating a number of unique ad impressions in a streaming video system includes defining parameters of an ad campaign and a desired number of ad impressions for the campaign. A computer system determines a discrete probability distribution of video advertising segments per unit time per client device in a population of video advertising segments streamed to a plurality of client devices, based on historical data. The system randomly samples the probability distribution without replacement, based on the defined number of desired ad impressions. An enhanced binary search algorithm may be used for the sampling. Each sample of the probability distribution identifies a number of ads streamed to a different client device in the probability distribution. The system determines, based on the sampling, a number of unique client devices included the samples, thus obtaining an estimate of unique ad impressions for the defined ad campaign.

Abstract: Estimating ad inventory in an online video streaming system accurately handles interdependencies among targeted attributes. The estimating includes determining a probability distribution of samples taken from electronic data records of ad impressions in an interactive online video streaming service, among a population comprising each different combination of targeted attributes recorded for each of the samples. In addition, the estimating may include populating an electronic data structure with data relating the each different combination of the targeted attributes to corresponding forecasted number of available impressions in a defined time period, based on the probability distribution and a forecasted total number of available impressions in the time period.