Abstract: A method for targeted content insertion in real-time streaming. The method leverages the different ordered signals available on a multi-step process that clients follow when subscribing to a real-time media (video and/or audio) stream including selecting a region, connecting to a control channel, and subscribing to a stream to guide a multi-stage process of content selection, transcoding, and insertion in a real-time media stream. A system is also specified for implementing the described method on a real-time streaming architecture.

Abstract: A set of techniques leverages a real-time version of a media stream to gather information on its content which information is used to improve or augment the service delivery of other versions (e.g., live or video-on-demand) of the same stream. These techniques take advantage of the fact that the real-time version of a stream is sufficiently ahead, in time, from any other version to enable the gathering and use of information on the stream content.

Abstract: The following describes techniques to maintain continuity of a live media stream. A media stream includes a first distribution layer with a first set of key frames and a set of delta frames succeeding key frames in the first set of key frames. The media stream also includes a second distribution layer including a second set of key frames, where the second set of key frames contains fewer key frames than the first set of key frames. Using the first and second distribution layers, a client device may subscribe to the media stream with a minimized start-up time and optimized quality of experience among varying complexity of the media content and dynamic network conditions.

Abstract: A method for targeted content insertion in real-time streaming. The method leverages the different ordered signals available on a multi-step process that clients follow when subscribing to a real-time media (video and/or audio) stream including selecting a region, connecting to a control channel, and subscribing to a stream to guide a multi-stage process of content selection, transcoding, and insertion in a real-time media stream. A system is also specified for implementing the described method on a real-time streaming architecture.

Abstract: A client device is communicatively coupled to a platform that distributes live streams over a network to multiple client devices. The live streams provide a live broadcast of a live event from multiple viewpoints captured by source devices including a first source device. A first live stream is provided to the client device, which renders a first viewpoint captured from the first source device. The client device is displays a second viewpoint with a different vantage point by rendering a second live stream of a second source device. The second live stream is delayed for rendering until a beginning of a next scene of the first live stream.

Abstract: A real-time streaming service predicts an incoming flash crowd event and manages computing resources to respond to the event before traffic peaks, thus reducing the likelihood that the streaming service's resources will be overwhelmed. Embodiments of a real-time streaming server predict a flash crowd event by detecting actions by client devices during a multi-step process to access a real-time content stream from an endpoint server cluster. Initially, the endpoint server has first computing resources configured to stream the content stream to the client devices. The streaming server provisions second computing resources at the endpoint server based on a rate at which the client devices perform an action associated with a first step in the multi-step process. The second computing resources are configured to stream the real-time content stream based on a rate at which the client devices perform an action associated with a second step in the multi-step process.

Abstract: The following describes techniques to maintain continuity of a live media stream. A media stream includes a first distribution layer with a first set of key frames and a set of delta frames succeeding key frames in the first set of key frames. The media stream also includes a second distribution layer including a second set of key frames, where the second set of key frames contains fewer key frames than the first set of key frames. Using the first and second distribution layers, a client device may subscribe to the media stream with a minimized start-up time and optimized quality of experience among varying complexity of the media content and dynamic network conditions.

Abstract: A client device is communicatively coupled to a platform that distributes live streams over a network to multiple client devices. The live streams provide a live broadcast of a live event from multiple viewpoints captured by source devices including a first source device. A first live stream is provided to the client device, which renders a first viewpoint captured from the first source device. The client device is displays a second viewpoint with a different vantage point by rendering a second live stream of a second source device. The second live stream is delayed for rendering until a beginning of a next scene of the first live stream.

Abstract: A real-time streaming service predicts an incoming flash crowd event and manages computing resources to respond to the event before traffic peaks, thus reducing the likelihood that the streaming service's resources will be overwhelmed. Embodiments of a real-time streaming server predict a flash crowd event by detecting actions by client devices during a multi-step process to access a real-time content stream from an endpoint server cluster. Initially, the endpoint server has first computing resources configured to stream the content stream to the client devices. The streaming server provisions second computing resources at the endpoint server based on a rate at which the client devices perform an action associated with a first step in the multi-step process. The second computing resources are configured to stream the real-time content stream based on a rate at which the client devices perform an action associated with a second step in the multi-step process.

Abstract: A real-time streaming service predicts an incoming flash crowd event and manages computing resources to respond to the event before traffic peaks, thus reducing the likelihood that the streaming service's resources will be overwhelmed. Embodiments of a real-time streaming server predict a flash crowd event by detecting actions by client devices during a multi-step process to access a real-time content stream from an endpoint server cluster. Initially, the endpoint server has first computing resources configured to stream the content stream to the client devices. The streaming server provisions second computing resources at the endpoint server based on a rate at which the client devices perform an action associated with a first step in the multi-step process. The second computing resources are configured to stream the real-time content stream based on a rate at which the client devices perform an action associated with a second step in the multi-step process.

Abstract: The following describes techniques to maintain continuity of a live media stream. A media stream includes a first distribution layer with a first set of key frames and a set of delta frames succeeding key frames in the first set of key frames. The media stream also includes a second distribution layer including a second set of key frames, where the second set of key frames contains fewer key frames than the first set of key frames. Using the first and second distribution layers, a client device may subscribe to the media stream with a minimized start-up time and optimized quality of experience among varying complexity of the media content and dynamic network conditions.

Abstract: A method for targeted content insertion in real-time streaming. The method leverages the different ordered signals available on a multi-step process that clients follow when subscribing to a real-time media (video and/or audio) stream including selecting a region, connecting to a control channel, and subscribing to a stream to guide a multi-stage process of content selection, transcoding, and insertion in a real-time media stream. A system is also specified for implementing the described method on a real-time streaming architecture.

Abstract: A real-time streaming service predicts an incoming flash crowd event and manages computing resources to respond to the event before traffic peaks, thus reducing the likelihood that the streaming service's resources will be overwhelmed. Embodiments of a real-time streaming server predict a flash crowd event by detecting actions by client devices during a multi-step process to access a real-time content stream from an endpoint server cluster. Initially, the endpoint server has first computing resources configured to stream the content stream to the client devices. The streaming server provisions second computing resources at the endpoint server based on a rate at which the client devices perform an action associated with a first step in the multi-step process. The second computing resources are configured to stream the real-time content stream based on a rate at which the client devices perform an action associated with a second step in the multi-step process.

Abstract: A technique for programmatically obtaining experimental measurements for model construction. A user provides criteria for the model, such as computational algorithms which characterize behavior of the real system, specifications of experiments to be performed on the real system for collecting experimental data from the real system, an identification of sought parameters which are to be derived from results of the experiments and desired tolerance constraints on the sought parameters. From experimental data collected from the real system and from the provided criteria, the inventive method and apparatus programmatically determines in an iterative loop which additional experiments are to be performed in order to achieve the desired tolerance constraints. After one or more iterations of the loop, the values for the sought parameters are determined within the desired tolerance constraints.