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.

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 method and system for Precision Drive Testing (PDT) of cellular wireless networks (e.g., 2G/3G/4G/LTE, 5G, 6G). The PDT system and method are cloud-based, automated, remotely controlled and allow for directed testing of user/device or machine experience of network(s) and service(s) performance by running pre-defined use cases, specific test scripts on pre-defined (pre-calculated) routes or within pre-defined (pre-calculated) areas of interest for the use case. The PDT may be automatically and remotely triggered by AI/ML based network sensing by drive test agents and/or outcomes (results) of network and/or service performance data and/or customer analytics or raw network and/or service and/or customer data, and/or network planning data, and/or new deployments of networks/technologies/services/devices, and benchmarking data.

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).

Abstract: Various methods, systems, and computer program products are disclosed for determining a location estimate of user equipment in a mobile network. For example, a method may include receiving measurement data that includes a time of arrival indication of data communicated between the user equipment and base stations of the mobile network. The method may further include processing only the received measurement data that is within a particular time window and excluding some of the received measurement data outside the particular time window. The method may further include determining a sync value based on the received measurement data within the time window. The sync value may synchronize timing between at least two base stations of the mobile network. The method may include determining a location of the user equipment based on the sync value and the measurement data. The method may include geometric or linear optimization of the determined location estimate.

Abstract: Various methods, systems, and computer program products are disclosed for determining a location estimate of user equipment in a mobile network. For example, a method may include receiving measurement data that includes a time of arrival indication of data communicated between the user equipment and base stations of the mobile network. The method may further include processing only the received measurement data that is within a particular time window and excluding some of the received measurement data outside the particular time window. The method may further include determining a sync value based on the received measurement data within the time window. The sync value may synchronize timing between at least two base stations of the mobile network. The method may include determining a location of the user equipment based on the sync value and the measurement data. The method may include geometric or linear optimization of the determined location estimate.