T-mobile Usa, Inc.

Jeffery A Smith

Jie Hui

Himesh Bagley

Peter P Myron

Kranthi Sontineni

Zachary William McPherson

Antoine T Tran

Paul Bongaarts

Arundhati Ghosh

Salvador Mendoza

Rahul Pal

Andrew Gapin

Ruchir Sinha

Kameswaran Arunachalam

Taha Najeeb

Anandajothi Muttayane

Kelly Schiller

Yun-Ling Wang

Geeta Khetan

Rajesh Gulati

Methods, apparatus, and systems for providing deterministic network experience to application-level services are disclosed. In one example aspect, a method includes providing a set of interfaces to an application-level service deployed in part on a user device. The set of interfaces is configured to provide a notification to the application-level service regarding a real-time change in network conditions relevant to the application-level service. The method also includes receiving a registration request from the application-level service to register for the notification, estimating the network conditions based on physical layer information, and notifying the registered application-level service of the change in the network conditions based on the estimated network conditions.

Real-time network condition estimations for mobile devices

Systems and methods for classifying a traffic flow on a network to determine a policy are described herein. The systems and methods enable a network to use aspects of an encrypted traffic flow and compare those aspects against a training set of data to classify the traffic flow. The classification helps the network determine and enforce policies associated with traffic flows from various services, even if the traffic flows are encrypted (obfuscated).

Systems and methods for traffic classification

Systems and methods for classifying a traffic flow on a network to determine parameters are described herein. The systems and methods use a policy API of a cellular network to allow entities to self-identify when, for example, their traffic flows may be encrypted or the identity of the originating location is obfuscated. The requesting entity accesses the policy API and transmits a self-identification request for a particular set of parameters to be used for subsequent traffic flows from the self-identification requesting entity. The cellular network may use the self-identification requests to allocate resources according to the parameters listed in the self-identification request without requiring the cellular network to decrypt the transmissions or analyze the transmissions to determine parameters.

Systems and methods discussed herein are directed to combining multiple network flows with respect to usage of applications into sessions. A record of a plurality of network flows may be obtained where the record relates to usage of the application by a user device in a wireless communication network. The plurality of network flows may be combined into a session, where an earliest start time of a network flow represents a session start time for the session and a latest end time of a network flow represents a session end time for the session. Based at least in part on the session start time and the session end time, a usage of the application during the session by the user device may be determined. Based at least in part on a plurality of sessions, usage of the application in the wireless communication network may be estimated.

Sessionization of network flows in a wireless communication network

A computing system may automatically classify applications that are used via a communication network. Application classification may include identifying a signature or group of signatures that belongs to an application or service associated with data flow through a network. The computer system of the network may collect data regarding the application from a mobile device, from the network, and/or from a digital distribution service accessible via the network. The system may combine such data together to identify and classify the application.

Application classification

A first time that a Domain Name System (DNS) response message is sent by the gateway server of a network to a user device may be determined, the DNS response message being generated by a Local DNS server of the network in response to a DNS request message from the user device for routing via the gateway server to the user device. A second time that a client handshake message for an external server identified by a network address in the DNS response is received at the gateway server may be determined, in which the client handshake message is sent by the user device. A roundtrip time for the network that is a time difference between the first time and the second time is calculated. The roundtrip trip time is then halved to obtain a network latency time measurement for the network.

Network latency time measurement using DNS and web server messages

Techniques for network scheduling that may provide consistent latency in content delivery are described. For example, the techniques may include receiving, by a scheduler of a carrier network, a consistent latency request associated with an application operating on a user equipment (UE), the consistent latency request including a specified latency value. Based at least in part on the specified latency value, the scheduler of the carrier network may schedule transmission of one or more packets associated with the application operating on the UE to cause the one or more packets to arrive at the UE with an inter-packet delay substantially equal to the specified latency value.

Network latency control

The techniques described herein involve analysis of communication data included in trace file(s) of device(s) involved in a communication. These trace file(s) may each include data associated with multiple layers of a communication protocol stack of a respective device or data associated with a single such layer. The techniques may further involve one or more of determination of performance metrics associated with data at a specific layer of a specific device, correlation of the data between layers of a device, or correlation of data across multiple device(s) involved in the communication. The performance metrics or correlated data may then be analyzed based on thresholds or models to determine whether the performance metrics or correlated data exhibits a degraded quality of user experience. Also or instead, graphic or textual representations of the performance metrics or correlated data may be generated.

Quality of user experience analysis

Techniques and methods for dynamically tuning of content delivery over a communication network are described. In one aspect, a method of dynamically tuning of content delivery over a communication network may involve a user device detecting an event related to a content delivery traffic from an application server to the user device over the communication network. The method may also involve the user device communicating the detected event to the application server.

Network aware dynamic content delivery tuning

The techniques described herein detect which one of multiple different radio states a radio communication unit of a client device is currently operating in. The techniques may also learn one or more transition parameters that, once satisfied or reached, may switch the radio communication unit from one radio state to another radio state. Using the detected radio state and the learned transition parameters, the client device can optimize network communications.

Mobile network traffic optimization

Buffering streaming content includes accessing prior device location data of a device and predicting a future sector that the device will travel through based at least in part on the prior device location data. A predicted quality of service of wireless communications is determined and a streaming buffer is adjusted based at least in part on the predicted quality of service.

Dynamic predictive buffering

A system and method of optimizing video content is provided. A request for data is received by an Application Delivery Controller from a user device (UD). Upon determining that the request for data is related to video content, an operating mode message is sent to an element of the packet core network to configure the element of the packet core network to provide the video content by transmitting a predetermined volume of data of the video content in burst mode to a memory buffer of the UD. Upon completion of the burst mode, a continuation of a data related to the video content is transmitted at a trickle mode that is slower than the burst mode. The cycle continues between burst mode and trickle mode for every trigger event.

Growing community of inventors

Bothell, WA, United States of America

Jeffery A Smith