Roku, Inc.

Activevideo Networks, Inc.

Andrey Marsavin

Wim Michiels

Govind Vaidya

Beth Teresa Logan

Lena Pavlovskaia

Vijay Anand Raghavan

Tapan Oza

Zheng Han

Amit Paliwal

Charles Lawrence

Joshua Dahlby

Mark Tom

Natalie Regis

Rodrigo Emiliano Tocalini

Robert Fitzgerald Watts

Lena Y Pavlovskaia

Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for performing adaptive audio bitrate selection. An example embodiment operates by receiving a data streaming request. The embodiment then streams data at a first audio bitrate. The embodiment then determines whether there is a streaming discontinuity associated with the data. The embodiment then determines, in response to the determination that there is a streaming discontinuity, whether a network condition satisfies one or more conditions based on one or more streaming parameters. The embodiment then, in response to the determination that the network condition satisfies the one or more conditions, adjusts the first audio bitrate to a second audio bitrate during the streaming discontinuity. The embodiment then continues to stream the data, subsequent to the streaming discontinuity, at the second audio bitrate to complete the data streaming request.

Adaptive audio bitrate selection

Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for modifying one or more parameters of a data streaming bitrate selection algorithm based on machine learning. An example embodiment operates by training and operating a first machine learning model to predict a sustainable network bandwidth. A second machine learning model is trained to receive the sustainable network bandwidth and predict a likelihood that this network bandwidth will not empty a data buffer of streaming data. A bitrate is selected based on the likelihood being below a threshold percentage, such as 50%.

Machine learning for adaptive bitrate selection

A client device receives a broadcast content signal containing an interactive identifier over a managed network at a client device. The interactive identifier may be a trigger that is included in a header or embedded within the digital video data. The trigger may have a temporal component, wherein the trigger can expire after a certain period of time. In response to identification of the trigger, the client device sends a user request for interactive content over an unmanaged network. For example, the managed network may be a one-way satellite television network, IP-television network or cable television network and the unmanaged network may be the Internet. The client device switches between receiving data from the managed network to receiving data from the unmanaged network.

Providing television broadcasts over a managed network and interactive content over an unmanaged network to a client device

Systems and methods are presented that permit an individual to share digital video between video devices using a cable television system. A first video device streams digital video to a cable headend, which transcodes it and stitches it with other video content, such as a menuing system. The headend then transmits the digital video to a second video device, such as a set top box, for display. The data stream may be controlled using a standard set top box remote control, and the system may be used without purchasing additional hardware or software, or configuring a wireless local area network.

Streaming digital video between video devices using a cable television system

Growing community of inventors

San Jose, CA, United States of America

Andrey Marsavin