Uncertain personal advertisement allocation for Mobile TV

Adany, Ron; Kraus, Sarit; Ordóñez, Fernando

doi:10.1145/1971519.1971548

Cited by 1 publication

(3 citation statements)

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“…A summary is presented in section 5. References in the current literature ( [1], [2], [6]) do not apply directly to the exact circumstance described so we understand our methods to be novel in the application area.…”

Section: Overview Of the Addressable Advertising Modelmentioning

confidence: 99%

“…Note that the term ℓ reflects the number of networks going into break in an overlapping minute and 1 ℓ accounts for the random allocation of the resource.…”

Section: Greedy Algorithm Formulation For One Resourcementioning

confidence: 99%

“…By considering multicast to be similar to broadcasting, and taking into account the bandwidth constraints of carriers as akin to available channel constraints of traditional television distribution, we can transfer results from the field of fixed channel delivery to solving the problems of mobile TV. Herein, we use the language of traditional TV delivery, but under general assumptions of resource constraint the results apply as well to mobile TV ( [1], [2]).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Bandwidth and Resource Allocation Optimization Through a Probabilistic Algorithm for Mobile TV

Sadeghi

Mizera

2017

Proceedings of the 15th International Conference on Advances in Mobile Computing &Amp; Multimedia - MoMM2017

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In this paper we consider the problem of bandwidth and resource allocation optimization for delivering addressable advertising in traditional TV. Transmitting addressable advertising over a mobile TV platform via LTE broadcast with eMBMS, which is capable of efficiently supporting a large number of concurrent users within available network and spectrum constraints, has relevant structural similarities to delivering this advertising over traditional television systems, and so these results from the one area transfer to the other. We introduce a probabilistic algorithm for resource optimization and detail its exact recursive O(n 2) (in the number of delivered programming networks) implementation together with a practical approximation. The proposed methods are evaluated on their performance against real historical data on TV programming and viewing, with the new methods showing significant improvement in terms of advertising revenue over methods currently used in the industry. CCS CONCEPTS • Mathematics of computing → Probabilistic algorithms; Stochastic processes; Nonparametric statistics; • Computing methodologies → Gaussian processes;

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Section: Overview Of the Addressable Advertising Modelmentioning

confidence: 99%

“…Note that the term ℓ reflects the number of networks going into break in an overlapping minute and 1 ℓ accounts for the random allocation of the resource.…”

Section: Greedy Algorithm Formulation For One Resourcementioning

confidence: 99%