Abstract: In various examples, the decoding and upscaling capabilities of a client device are analyzed to determine encoding parameters and operations used by a content streaming server to generate encoded video streams. The quality of the upscaled content of the client device may be monitored by the streaming servers such that the encoding parameters may be updated based on the monitored quality. In this way, the encoding operations of one or more streaming servers may be more effectively matched to the decoding and upscaling abilities of one or more client devise such that an increased number of client devices may be served by the streaming servers.

Abstract: A game-agnostic event detector can be used to automatically identify game events. Game-specific configuration data can be used to specify types of pre-processing to be performed on media for a game session, as well as types of detectors to be used to detect events for the game. Event data for detected events can be written to an event log in a form that is both human- and process-readable. The event data can be used for various purposes, such as to generate highlight videos or provide player performance feedback.

Abstract: In various examples, the decoding and upscaling capabilities of a client device are analyzed to determine encoding parameters and operations used by a content streaming server to generate encoded video streams. The quality of the upscaled content of the client device may be monitored by the streaming servers such that the encoding parameters may be updated based on the monitored quality. In this way, the encoding operations of one or more streaming servers may be more effectively matched to the decoding and upscaling abilities of one or more client devise such that an increased number of client devices may be served by the streaming servers.

Abstract: A game-agnostic event detector can be used to automatically identify game events. Game-specific configuration data can be used to specify types of pre-processing to be performed on media for a game session, as well as types of detectors to be used to detect events for the game. Event data for detected events can be written to an event log in a form that is both human- and process-readable. The event data can be used for various purposes, such as to generate highlight videos or provide player performance feedback.

Abstract: In various examples, potentially highlight-worthy video clips are identified from a gameplay session that a gamer might then selectively share or store for later viewing. The video clips may be identified in an unsupervised manner based on analyzing game data for durations of predicted interest. A classification model may be trained in an unsupervised manner to classify those video clips without requiring manual labeling of game-specific image or audio data. The gamer can select the video clips as highlights (e.g., to share on social media, store in a highlight reel, etc.). The classification model may be updated and improved based on new video clips, such as by creating new video-clip classes.

Abstract: In various examples, the decoding and upscaling capabilities of a client device are analyzed to determine encoding parameters and operations used by a content streaming server to generate encoded video streams. The quality of the upscaled content of the client device may be monitored by the streaming servers such that the encoding parameters may be updated based on the monitored quality. In this way, the encoding operations of one or more streaming servers may be more effectively matched to the decoding and upscaling abilities of one or more client devise such that an increased number of client devices may be served by the streaming servers.

Abstract: In various examples, potentially highlight-worthy video clips are identified from a gameplay session that a gamer might then selectively share or store for later viewing. The video clips may be identified in an unsupervised manner based on analyzing game data for durations of predicted interest. A classification model may be trained in an unsupervised manner to classify those video clips without requiring manual labeling of game-specific image or audio data. The gamer can select the video clips as highlights (e.g., to share on social media, store in a highlight reel, etc.). The classification model may be updated and improved based on new video clips, such as by creating new video-clip classes.

Abstract: Approaches presented herein provide for dynamic selection and presentation of content for a broadcast or transmission, such as to provide content that is most likely to be of interest to a viewer. This can be accomplished, at least in part, by predicting occurrences of events of interest in one or more sources of content, such as one or more input media streams. The occurrences can be predicted using various sources of content or data, as may include non-game video, player input, and player-agnostic game data. Various input data streams can be analyzed to predict the probability of one or more events occurring over a future period of time, and these probabilities can be used to assign priority values to the various streams. These priority values can be used to determine which streams to include in a broadcast, as well as how to arrange or feature those streams in the broadcast.

Abstract: Apparatuses, systems, and techniques to predict events within a gameplay session and modify a game broadcast based at least in part on the predicted events. In at least one embodiment, events within the gameplay session are predicted, which once predicted, cause assets to be generated and once the event is detected the assets are included in the game broadcast.

Abstract: In various examples, recordings of gameplay sessions are enhanced by the application of special effects to relatively high(er) and/or low(er) interest durations of the gameplay sessions. Durations of relatively high(er) or low(er) predicted interest in a gameplay session are identified, for instance, based upon level of activity engaged in by a gamer during a particular gameplay session duration. Once identified, different variations of video characteristic(s) are applied to at least a portion of the identified durations for implementation during playback. The recordings may be generated and/or played back in real-time with a live gameplay session, or after completion of the gameplay session. Further, video data of the recordings themselves may be modified to include the special effects and/or indications of the durations and/or variations may be included in metadata and used for playback.

Abstract: In various examples, recordings of gameplay sessions are enhanced by the application of special effects to relatively high(er) and/or low(er) interest durations of the gameplay sessions. Durations of relatively high(er) or low(er) predicted interest in a gameplay session are identified, for instance, based upon level of activity engaged in by a gamer during a particular gameplay session duration. Once identified, different variations of video characteristic(s) are applied to at least a portion of the identified durations for implementation during playback. The recordings may be generated and/or played back in real-time with a live gameplay session, or after completion of the gameplay session. Further, video data of the recordings themselves may be modified to include the special effects and/or indications of the durations and/or variations may be included in metadata and used for playback.

Abstract: A game-agnostic event detector can be used to automatically identify game events. Game-specific configuration data can be used to specify types of pre-processing to be performed on media for a game session, as well as types of detectors to be used to detect events for the game. Event data for detected events can be written to an event log in a form that is both human- and process-readable. The event data can be used for various purposes, such as to generate highlight videos or provide player performance feedback.

Abstract: In various examples, durations of relatively high user activity within a gameplay session may be determined from user input events using a running user activity measurement. Once a duration is identified, it may be further analyzed to merge the duration with one or more other durations and/or to determine or predict whether the duration would be of sufficient interest for further action. A user interest score for an identified duration may be computed based on a set of the user input events that occur in the duration and used to determine and/or predict whether the duration would be of sufficient interest for further action. In some cases, an action may be performed based on determining the user interest score is greater than a statistical value that is computed from user interest scores of multiple identified durations.

Abstract: In various examples, recordings of gameplay sessions are enhanced by the application of special effects to relatively high(er) and/or low(er) interest durations of the gameplay sessions. Durations of relatively high(er) or low(er) predicted interest in a gameplay session are identified, for instance, based upon level of activity engaged in by a gamer during a particular gameplay session duration. Once identified, different variations of video characteristic(s) are applied to at least a portion of the identified durations for implementation during playback. The recordings may be generated and/or played back in real-time with a live gameplay session, or after completion of the gameplay session. Further, video data of the recordings themselves may be modified to include the special effects and/or indications of the durations and/or variations may be included in metadata and used for playback.

Abstract: A game-agnostic event detector can be used to automatically identify game events. Game-specific configuration data can be used to specify types of pre-processing to be performed on media for a game session, as well as types of detectors to be used to detect events for the game. Event data for detected events can be written to an event log in a form that is both human- and process-readable. The event data can be used for various purposes, such as to generate highlight videos or provide player performance feedback.

Abstract: In various examples, durations of relatively high user activity within a gameplay session may be determined from user input events using a running user activity measurement. Once a duration is identified, it may be further analyzed to merge the duration with one or more other durations and/or to determine or predict whether the duration would be of sufficient interest for further action. A user interest score for an identified duration may be computed based on a set of the user input events that occur in the duration and used to determine and/or predict whether the duration would be of sufficient interest for further action. In some cases, an action may be performed based on determining the user interest score is greater than a statistical value that is computed from user interest scores of multiple identified durations.

Abstract: In various examples, potentially highlight-worthy video clips are identified from a gameplay session that a gamer might then selectively share or store for later viewing. The video clips may be identified in an unsupervised manner based on analyzing game data for durations of predicted interest. A classification model may be trained in an unsupervised manner to classify those video clips without requiring manual labeling of game-specific image or audio data. The gamer can select the video clips as highlights (e.g., to share on social media, store in a highlight reel, etc.). The classification model may be updated and improved based on new video clips, such as by creating new video-clip classes.

Abstract: In various examples, potentially highlight-worthy video clips are identified from a gameplay session that a gamer might then selectively share or store for later viewing. The video clips may be identified in an unsupervised manner based on analyzing game data for durations of predicted interest. A classification model may be trained in an unsupervised manner to classify those video clips without requiring manual labeling of game-specific image or audio data. The gamer can select the video clips as highlights (e.g., to share on social media, store in a highlight reel, etc.). The classification model may be updated and improved based on new video clips, such as by creating new video-clip classes.

Abstract: A game-agnostic event detector can be used to automatically identify game events. Game-specific configuration data can be used to specify types of pre-processing to be performed on media for a game session, as well as types of detectors to be used to detect events for the game. Event data for detected events can be written to an event log in a form that is both human- and process-readable. The event data can be used for various purposes, such as to generate highlight videos or provide player performance feedback.

Abstract: In various examples, durations of relatively high user activity within a gameplay session may be determined from user input events using a running user activity measurement. Once a duration is identified, it may be further analyzed to merge the duration with one or more other durations and/or to determine or predict whether the duration would be of sufficient interest for further action. A user interest score for an identified duration may be computed based on a set of the user input events that occur in the duration and used to determine and/or predict whether the duration would be of sufficient interest for further action. In some cases, an action may be performed based on determining the user interest score is greater than a statistical value that is computed from user interest scores of multiple identified durations.