95 results about "Streaming application" patented technology

In various examples, each user of a hosted application may be modeled with a corresponding user-specific resource consumption model that predicts a measure of that user's anticipated (e.g., processing, memory or storage, and / or networking) resource utilization at some future time based on an input representation of one or more features of the user's current session, application (e.g., game) setup, skill or experience level, social media activity, some time series representing feature(s) for a series of interactions, the time of day, and / or other characteristics. As such, user-specific models may be used to predict resource demands at a future time and schedule resource allocations accordingly. The present techniques may be used to manage and reallocate resources for applications such as game streaming applications, remote desktop applications, simulation applications (e.g., an autonomous or semi-autonomous vehicle simulation), virtual reality (VR) and / or augmented reality (AR) streaming applications, and / or other application types.

A computer-implemented method for real-time creative creation includes retrieving a creative definition object (CDO) that contains a set of instructions related to one or more creative assets, each associated with a set of layout and behavior instructions, retrieving the assets, rendering the assets according to the set of instructions and displaying the output. A computer-implemented system for real-time creative creation includes at least one memory and one processor communicatively coupled to the memory and a rendering module activated by a streaming application equipped with a video player. The rendering module is configured to retrieve a CDO that contains instructions related to one or more creative assets, each creative asset associated with a set of layout and behavior instructions, retrieve the creative assets, render them according to the set of instructions and display the rendered creative on a display.

One of the promises of ATSC 3.0 has been the potential for data offload. Separately, advances have been made in hybrid and IP channel rollouts on ATSC 3.0 over the last year. The two have been combined to architect and implement a data offload system. This application explores a practical hybrid delivery model for streaming video services, allowing the distribution of video over CDNs and simultaneously over 3.0, drastically decreasing the bandwidth needs of these CDNs in 3.0 markets, all while integrating into third-party streaming apps to enable seamless streaming with no change to the viewer experience. Topics covered include the methodology for synchronization, encoding needs for the CDN and airchain, signaling design, integration into a streaming application, and the results of the real-world testing of this system.

In various examples, techniques for optimizing object detection models are described herein. Systems and methods are disclosed that process sensor data using a backbone of a machine learning model(s) in order to generate feature maps at different resolutions. The systems and methods then use the machine learning model(s) to generate a vector based at least in part on one or more of the feature maps. For example, if the backbone generates four feature maps, then the machine learning model(s) may generate the vector using two feature maps from the four feature maps. The systems and methods then process the vector using a transformer of the machine learning model(s) in order to generate data representing a class label(s) for an object(s) depicted by an image represented by the sensor data and / or a location(s) of the object(s) within the image.

In various examples, each hosted application may be modeled with a corresponding application-specific resource consumption model that predicts a measure of that application's anticipated resource utilization at some future time based on an input representation of one or more features of the current state of an instance of the hosted application. For cloud gaming, those features may include the current level being played, current obstacles, user results playing the level or obstacles, metadata quantifying one or more aspects of the level or obstacles, game progress, etc. As such, application-specific models may be used to predict resource demands at a future time and schedule resource allocations accordingly. The present techniques may be used to manage and reallocate resources for applications such as game streaming applications, remote desktop applications, simulation applications (e.g., an autonomous or semi-autonomous vehicle simulation), virtual reality (VR) and / or augmented reality (AR) streaming applications, and / or other application types.

The invention discloses a real-time streaming application monitoring method and device, a medium and a product. The method comprises the following steps: positioning a target operator according to configuration information; in the running process, when a trigger instruction is detected every time, the generation time of the trigger instruction is used as starting time, and the accumulated data processing quantity and the accumulated processing time are monitored in real time; when the accumulated data processing quantity is greater than or equal to a quantity threshold value, or the accumulated processing time is greater than or equal to a time threshold value, ending the monitoring process, constructing a monitoring period according to the ending time and the starting time, calculating the number of transactions per second and the delay time in the monitoring period, storing the transactions per second and the delay time in a distributed cache, and generating a new trigger instruction; according to the transaction number per second and the time delay time in the cache, a transaction number per second performance curve and a time delay performance curve are obtained and displayed, visual display of the performance is achieved, through distributed cache, the production operation risk caused by random address change after the real-time streaming application is restarted is reduced, and the monitoring stability and reliability are improved.

A mobile device receives network information from a connected non-terrestrial network. A streaming app installed on the mobile device is configured to stream data at a first bit rate while the mobile device is connected to a terrestrial network. The mobile device identifies network resources of the non-terrestrial network that include a bandwidth, a latency, and / or a data packet loss rate. The mobile device configures the streaming application in accordance with the network resources to stream data at a second bit rate lower than the first bit rate while the mobile device is connected to the non-terrestrial network. The streaming app is configured in accordance with a quality of service provided by the non-terrestrial network that is based on a frequency range, a congestion level, and / or a signal strength of the non-terrestrial network.

A technique for generating component usage statistics involves associating components with blocks of a stream-enabled application. When the streaming application is executed, block requests may be logged by Block ID in a log. The frequency of component use may be estimated by analyzing the block request log with the block associations.

An apparatus and method for efficiently performing efficient data storage and data transfer of machine learning data. In various implementations, a host processing circuit of a computing system executes a machine learning (ML) application. The application includes a computational graph that indicates the computational order of the ML nodes, layers, and stages of the ML model. The host processing circuit translates function calls in the application to commands particular to an accelerator circuit. The accelerator circuit preloads weights to be used by ML nodes of the ML model by retrieving compressed weights from a storage device different from system memory. The accelerator circuit uses a streaming application programming interface (API) and bypasses the host processing circuit to retrieve the compressed weights from the storage device. The accelerator circuit decompresses the retrieved weights and executes the ML node using the decompressed weights.

This application relates to the fields of cloud gaming and remote streaming display, and in particular to a boot-up method, apparatus, device, and medium. The method includes: in the initramfs stage, a startup animation program determines the optimal display mode, which can be uniformly displayed in subsequent display processes. Furthermore, during the control switching process in each stage, the current frame of the buffer of the previous program is continuously output and used as the first output buffer frame of the subsequent program, ensuring screen consistency and achieving smooth connection of multi-stage display. An early display proxy process and a parallel initialization mechanism are introduced to achieve continuous visual output across the entire link from system power-on to the display of the first frame of the target streaming application, effectively avoiding black screen, flickering, and screen jump phenomena during startup, and significantly improving the user's perceived experience of system startup speed and visual quality.

According to an aspect, a method includes rendering a user interface on an extended reality device, the user interface identifying a media item for playback using a streaming application, in response to selection of the media item for playback, initiating a display of immersive imagery related to the media item on the extended reality device, and transmitting a request to the streaming application. The request includes at least one parameter about the immersive imagery that causes the streaming application to render a display panel within the immersive imagery using the at least one parameter, the display panel displaying content of the media item.

Disclosed are apparatuses, systems, and techniques for implementing fractionalized data transfers between processing devices in real-time data generating and streaming applications. The techniques include but are not limited to processing, by a first processing device, image data to generate a plurality of portions of an image, responsive to generating a first portion of the plurality of portions of the image, storing the first portion in a first memory device of the first processing device, setting a completion indicator for the first portion, and causing the first portion to be provided to a second processing device.

There may be provided a method of streaming multimedia content encoded using an enhancement coding scheme and a method of packaging multimedia content encoded using an enhancement coding scheme for streaming, comprising retrieving an constructing a manifest file, the manifest file comprising metadata for constructing a locator to segments of the encoded multimedia content, the metadata comprising: a first representation pointing to a first version of the multimedia content encoded using a base coding scheme; and, location metadata for a first enhancement layer of the encoded multimedia content associated with the first version. A client device, packager, system and computer readable medium are also provided.

Disclosed are apparatuses, systems, and techniques that implementing fractionalized data transfers between processing devices in real-time data generating and streaming applications. The techniques include but are not limited to processing, by a first processing device, an image data to generate a plurality of portions of an image, responsive to generating a first portion of the plurality of portions of the image, storing the first portion in a first memory device of the first processing device, setting a completion indicator for the first portion, and causing the first portion to be provided to a second processing device.

A method of controlling energy consumed by a multimedia streaming application executed in a multimedia streaming system, the method comprising: receiving (358), at an end-user device, a message including information representative of energy consumed by at least one node of the multimedia streaming system when applying at least one process on content having a first profile delivered by the multimedia streaming application prior to delivering the content having the first profile to the end-user device; and requesting (359), at the end-user device, content having a second profile based on the received information representative of the energy consumed by the at least one node.

Provided are a method and apparatus for processing speech data, and a device and a medium. In one method, in response to receiving speech data of a target object (810), subtitle text associated with the speech data is acquired (820); and the subtitle text is provided in an audio live-streaming page (830). By utilizing the exemplary implementation in the present disclosure, more visual elements can be presented in an audio live-streaming application, so that more abundant visual effects are provided, and a user is supported in understanding more information of audio live streaming.

The application relates to a train network optimization method, system and storage medium based on a streaming media application. The method comprises the following steps: monitoring the heartbeat state of a control node and the heartbeat state of a streaming media processor; when the heartbeat of the control node and / or the heartbeat of the streaming media processor is detected to be abnormal, migrating the streaming media container of the abnormal control node and / or the streaming media container where the abnormal streaming media processor is located to a normal control node and / or a normal streaming media processor; when the heartbeat of the control node and the corresponding streaming media processor is detected to be normal, sending streaming media data in the streaming media container of the normal control node to an Ethernet in a multicast mode to be transmitted to a video terminal connected to the Ethernet. The application can solve the technical problems of high streaming media application data flow delay, high bandwidth resource occupation, complicated application deployment and high application disaster recovery cost in the current embedded environment.

Monitor a stream of tuples to be processed by processing elements operating on one or more compute nodes of a streaming application. Each processing unit has one or more stream operators. Receive a program request to execute a set of program code instructions. Identify a streaming application target of the set of program code instructions based on the program request. During operation of the streaming application, encapsulate a portion of the streaming application using a general-purpose program execution structure. The structure is configured to receive and execute program code instructions outside of pre-configured operations of the streaming application. During operation of the streaming application, during a time period in which the portion is executed, in response to the program request, and based on the streaming application target, execute the set of program code instructions by the structure.

Techniques described herein are related to updating streaming application software. For example, data from the streaming application software are transformed into a format for an engine to generate one or more results. The one or more results may comprise information related to the data from the streaming application software.

A method and system for generating test cases for a streaming application is disclosed. An execution plan for testing a functionality of the streaming application is received. A machine-learning module determines a workflow data associated with the functionality using at least one of a user profile library, an intent library, a device library, an input devices library, and a navigation library. A first and a second portion of the workflow are categorized into converging patterns and diverging patterns based on consistencies and variations in the workflow respectively. A test components generation module generates a first set and a second set of test components from the converging and the diverging patterns. A unique test cases corresponding to each of the plurality of platforms, operating systems, end devices, user profiles, and versions of the streaming application is then generated at runtime.

This invention provides a program, an information processing device, an information processing method, and an information processing system that simplify the editing operations of streamed videos and improve the usability of streaming applications. [Solution] A program to be executed by an information processing device for distributing a video in which images of performers are placed in a background image or a virtual space background includes: a storage process for storing a plurality of object candidates, which are candidates for objects that constitute the video, and a plurality of background candidates, which are candidates for backgrounds; a configuration process for composing each of the plurality of background candidates using at least a portion of the plurality of object candidates; and a setting process for setting the selected background candidate as the background in the video in response to an operation by the video distributor to select one of the plurality of background candidates.

A mobile device receives network information from a connected non-terrestrial network. A streaming app installed on the mobile device is configured to stream data at a first bit rate while the mobile device is connected to a terrestrial network. The mobile device identifies network resources of the non-terrestrial network that include a bandwidth, a latency, and / or a data packet loss rate. The mobile device configures the streaming application in accordance with the network resources to stream data at a second bit rate lower than the first bit rate while the mobile device is connected to the non-terrestrial network. The streaming app is configured in accordance with a quality of service provided by the non-terrestrial network that is based on a frequency range, a congestion level, and / or a signal strength of the non-terrestrial network.

Techniques and solutions are described for encoding digital video files, such as for streaming applications. Data associated with the digital video file forms a dataset that can be characterized by a measure of the dataset's center, such as an average, and a spread of the dataset, such as a deviation, with respective to a bitrate over a duration of the digital video file. The measure of center and spread are used to calculate a deviation-adjusted bitrate. A deviation adjusted bitrate can be calculated for the entire digital video file, or for particular subsets of the digital video file, such as for segments of a duration forming units of video streaming Disclosed techniques can provide various advantages, including using a reduced bitrate for video or video portions as compared with an average or static bitrate, for lower-complexity video, or using a higher bitrate for video or video portions for higher-complexity video.

A method and system for generating test cases for a streaming application is disclosed. An execution plan for testing a functionality of the streaming application is received. A machine-learning modul

Systems and methods herein address reference frame selection in video streaming applications using one or more processing units to replace, during receipt of an encoded video stream, a first set of frames stored in a cache with a second set of frames based at least in part on an indication within the encoded video stream that the second set of frames includes a non-blurred frame (NBF).

Described herein are systems, methods, and media for managing streaming devices using machine learning models. In an embodiment, a method of managing streaming devices includes detecting that a streaming application is launched on a streaming device; recommending a set of software components for the streaming device, wherein the set of recommended software components includes the streaming application; determining a set of software components that are actually running on the streaming device; and closing at least one of the software components that are actually running but not in the set of recommended software components.

An apparatus and method for efficiently performing efficient data storage and data transfer of machine learning data. In various implementations, a host processing circuit of a computing system executes a machine learning (ML) application. The application includes a computational graph that indicates the computational order of the ML nodes, layers, and stages of the ML model. The host processing circuit translates function calls in the application to commands particular to an accelerator circuit. The accelerator circuit preloads weights to be used by ML nodes of the ML model by retrieving compressed weights from a storage device different from system memory. The accelerator circuit uses a streaming application programming interface (API) and bypasses the host processing circuit to retrieve the compressed weights from the storage device. The accelerator circuit decompresses the retrieved weights and executes the ML node using the decompressed weights.

Devices and methods for providing streaming data via a web page are disclosed herein. The device includes a processor, a memory, and at least one user input device. Computer-executable instructions are stored in the memory. The computer instructions can be executed by the processor to display a web page and a widget embedded in the web page via a display. The widget includes a streaming element for processing and displaying streaming data. The widget can be configured to communicate electronically with a streaming application that generated the streaming data via an application programming interface. The widget is associated with the streaming application by a unique stream key.

Disclosed in the present application are a video live-streaming method and apparatus, a video processing method and apparatus, and a storage medium, which methods are used for a live-streaming device. The video live-streaming method comprises: generating a push video to be pushed to a live-streaming platform; writing first video image data of the push video into a first device file deployed in an operating system, wherein the first device file corresponds to a first camera associated with a live-streaming application, and the live-streaming application corresponds to the live-streaming platform; and using the live-streaming application to read the first video image data from the first device file via the operating system, and transmitting the first video image data to the live-streaming platform.

Devices, systems, and methods for machine learning-based micro-category generation may include a method including identifying, for a respective cluster of content titles or items, a top-K most representative group of the content titles or items; generating, by a machine learning model, based on a first input prompt, the common theme of the top-K most representative group; generating, by the machine learning model, based on the second input prompt and filtering, a group of content titles or items matching the common theme; generating, by the machine learning model, based on a third input prompt, a name for the group of content titles or items matching the common theme; and presenting the group of content titles or items matching the common theme and the name for the group of content titles or items matching the common theme via a user interface of a streaming media application.