Abstract: A method and a system for measuring user experience with interactive OTT services delivered over mobile networks. The method entails procedures to develop a model to measure user experience of an interactive OTT service, while the system represents a software tool that implements the model and a technique to measure user experience of an interactive OTT service. The method is applicable to any interactive OTT service, while the system is directed to a particular interactive OTT service—a cloud mobile gaming service.
Type:
Application
Filed:
December 1, 2023
Publication date:
June 6, 2024
Applicant:
INFOVISTA SWEDEN AB
Inventors:
PER NICLAS ÖGREN, DIMITAR MINOVSKI, IRINA COTANIS, PER GUSTAF ANDERS JOHANSSON, HENRIQUE SOUZA ROSSI, KARAN MITRA, CHRISTER ÅHLUND
Abstract: A framework system and method for developing hybrid voice/video QoE predictors, which use both network/codec/client parameters as well as the voice/video reference sample(s). The prediction model/algorithm uses deep packet inspection to produce relevant input and therefore the network's impact on the voice/video QoE can be determined without recording actual media (voice/video) content. Therefore, the QoE predictors are neither solely media based as available perceptual metrics, nor available parametric based. The present hybrid voice/video QoE uses the reference/original media information, unlike prior art hybrid video only QoE which use the recorded media (video only).
Abstract: A framework system and method for developing hybrid voice/video QoE predictors, which use both network/codec/client parameters as well as the voice/video reference sample(s). The prediction model/algorithm uses deep packet inspection to produce relevant input and therefore the network's impact on the voice/video QoE can be determined without recording actual media (voice/video) content. Therefore, the QoE predictors are neither solely media based as available perceptual metrics, nor available parametric based. The present hybrid voice/video QoE uses the reference/original media information, unlike prior art hybrid video only QoE which use the recorded media (video only).
Abstract: A framework system and method for developing hybrid voice/video QoE predictors, which use both network/codec/client parameters as well as the voice/video reference sample(s). The prediction model/algorithm uses deep packet inspection to produce relevant input and therefore the network's impact on the voice/video QoE can be determined without recording actual media (voice/video) content. Therefore, the QoE predictors are neither solely media based as available perceptual metrics, nor available parametric based. The present hybrid voice/video QoE uses the reference/original media information, unlike prior art hybrid video only QoE which use the recorded media (video only).