Microsoft Technology Licensing, LLC

Other

David Chiyuan Chu

Alastair Wolman

Eduardo Alberto Cuervo Laffaye

Johannes Peter Kopf

Sergey Grizan

Yury Degtyarev

Kyungmin Lee

Paramvir Bahl

Eduardo Cuervo

Jie Jerry Liu

Thomas Moscibroda

Lin Zhong

Bernhard Reinert

Eric J Horvitz

Sharad Agarwal

Feng Zhao

Aman Kansal

Lenin Ravindranath Sivalingam

James Wiliamson Mickens

Li Zhuang

Kaisen Lin

Nicholas D Lane

Jing Zhao

Nissanka Arachchige Bodhi Priyantha

Brian J Smith

Gursharan Singh Sidhu

Romit Roy Choudhury

Roger Wattenhofer

Eduardo A Cuervo Laffaye

Abm Musa

Xuan Bao

Kaifei Chen

Tingxin Yan

Zengbin Zhang

Xiaomeng Chen

Kevin Boos

Kiryong Ha

Jaebaek Seo

Zhiyong Tan

Xiangying Meng

Jian Qiu

Lara Deek

Jie Liu

Brian Smith

A server may be configured to receive first pose data from a virtual reality device, generate second pose data, including a pose prediction, based at least on the first pose data, render a map representation based at least on the pose prediction, determine regions of the map representation based on a likelihood of view of each of the regions, perform foveation in each of the regions and generate an encoded frame based on the regions and a network parameter, and send the encoded frame and second pose data to the device. The encoding may associate each of the regions with a quality level and base the foveation on each region's associated quality level. The foveation may use different quantization parameters for each of the regions and/or use rendering foveation for each of regions. Network conditions such as latency, bandwidth, and/or jitter may be considered for bit allocation in the foveation.

Virtual reality with interactive streaming video and likelihood-based foveation

A server device and method are provided for use in predictive server-side rendering of scenes based on client-side user input. The server device may include a processor and a storage device holding instructions for an application program executable by the processor to receive, at the application program, a current navigation input in a stream of navigation inputs from a client device over a network, calculate a predicted future navigation input based on the current navigation input and a current application state of the application program, render a future scene based on the predicted future navigation input to a rendering surface, and send the rendering surface to the client device over the network.

Predictive server-side rendering of scenes

A head mounted display device including a processor configured to compute a rendered rendering surface of a predicted scene having a predicted user viewpoint, the predicted user viewpoint being a prediction of a viewpoint that a user will have at a point in time that was predicted for the user of the head mounted display device prior to the point in time, receive, from the user input device, a subsequent user navigation input near the point in time in the stream of user input, determine an actual user viewpoint based on the subsequent user navigation input, determine a user viewpoint misprediction based on the predicted user viewpoint and the actual user viewpoint, and reconstruct a viewport for the actual user viewpoint from the rendered rendering surface.

Reconstructing viewport upon user viewpoint misprediction

The present describes low latency streaming using temporal frame transformation. An execution component in an edge server executes a first instance of an application. A server interface component receives, from a remote server, a resolution delta frame indicating differences between a high resolution first frame and a low resolution first frame of a second instance of the application or, alternatively, receives the high resolution first frame. A video manipulation component generates a motion delta frame by identifying differences between a low resolution first frame and a low resolution second frame of the first instance of the application. The video manipulation component generates a high resolution transformed frame by applying the resolution delta frame and the motion delta frame to the low resolution second frame.

Low latency application streaming using temporal frame transformation

Virtual reality is provided by a mobile VR device. VR scenes are displayed by serving an application's image requests from a local cache of pre-rendered image frames. As the device user moves, image frames that match a VR scene viewed from a current pose (3D position and 3D orientation of the device translated to the virtual environment) are retrieved from the cache and displayed to the user as a VR scene. The cache may include a static cache including static image frames and a dynamic object cache including dynamic object image frames. The static cache may be indexed by the device current pose. The dynamic object cache may be indexed by the dynamic object's animation stage, orientation, and relative distance from the device user's current pose for a given time or movement-based trigger. Static image frames and dynamic object image frames may be composited, and displayed as a scene.

Stereoscopic virtual reality through caching and image based rendering

Nodes of a computing cluster can be selected to run new computing jobs while providing acceptable performance of jobs running on the nodes. Respective performance metrics of respective workloads on respective computing nodes can be determined. Each workload can include a new computing job and the performance metrics can be determined based at least in part on respective measured performance data of the ones of the computing nodes and information of the new computing job. Candidate ones of the computing nodes can be determined based at least in part on the respective performance metrics. One of the candidate computing nodes can be selected based at least in part on the information of the new computing job. In some examples, identification of the new computing job can be transmitted to the selected node. In some examples, state data of the nodes can be updated based on the performance data.

Multiple-computing-node system job node selection

In the new architecture, a client that desires access to a piece of hardware sends HTTP requests to a device server. The server receives a HTTP requests, accesses a device on behalf of the client, and send the device's response back to the client in the form of an HTTP response. This architecture has three primary advantages. First, it offers a clean interface for clients where all devices are accessed as if they are web servers. Second, it helps make devices more secure whereby clients can be prevented from accessing hardware directly, and all device access is forced through the narrow HTTP access protocol and checked for compliance with a desired security policy. Third, since HTTP allows local and remote servers to be contacted, the proposed architecture makes it easy for clients to communicate with devices that are not physically co-resident with the client but which are accessible via a network connection.

Accessing hardware devices using web server abstractions

A first user input is received when a client program executed by a client computing device is in a first state. The first user input is sent to a server computing device to render a view of a virtual scene. A state change from the first state in the client program due to a second user input or a program event is identified. One or more gaps in a server-rendered current view due to the state change are determined. A rendering of the one or more gaps is selected from among the server-rendered current view, a server-rendered predicted view and one or more prior-rendered views. A current view is rendered using a simplified model of the virtual scene by rendering the one or more gaps from the selected rendering. The current rendered view is visually presented via a display of the client computing device.

View rendering from multiple server-side renderings

An application may be augmented for application-centric socialization activities for a user of the application, based on instrumenting the application for the application-centric socialization activities. The application-centric socialization activities may be launched from within the augmented application on a device, the application-centric socialization activities determined based on one or more contexts associated with the user that are determined during execution of the augmented application on the device.

Application-centric socialization

The claimed subject matter includes techniques for live migration of a graphics processing unit (GPU) state. An example method includes receiving recorded GPU commands from a relay at a destination GPU. The method also includes replaying the recorded GPU commands at the destination GPU. The method also includes detecting a downtime for the GPU commands. The method further includes establishing a connection between the destination GPU and the client during the detected downtime.

Migration of graphics processing unit (GPU) states

Technologies pertaining to sharing an application installed on a mobile computing device with another computing device are described herein. An indication is received that the application is desirably shared with the another computing device. Responsive to receiving such indication, a communications channel is automatically established between the mobile computing device and the another computing device, and display data generated at the mobile computing device is transmitted to the another computing device by way of the communications channel. The display data is displayed on the another computing device.

Mobile computing device application sharing

Described is a technology by which GPS-capable devices work with a cloud service to receive satellite visibility-related data. The satellite visibility-related data may be used to determine a location, and/or to abort a search for satellites. The cloud service may use crowd data from other GPS-capable devices. In one aspect, line-of-sight satellites are differentiated from other satellites, and used to determine which satellite signals are more trustworthy. Reflected signals also may be determined.

Using satellite visibility data for improved location accuracy

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Redmond, WA, United States of America

David Chiyuan Chu