Abstract: A method and system for adaptive video streaming receives inputs by a deep learning neural network engine. The inputs may include a previous lookahead buffer size, previous download time(s), a current buffer size, next available stream size(s), a last chuck quality, a current buffer size, previous download speed(s), a previous lookahead buffer size, a number of chunks remaining, and/or network condition statistics based upon geolocation information. The inputs are processed by the deep learning neural network engine using a neural network machine learning model to adjust a capacity of a buffer lookahead.

Abstract: An embodiment provides a software system capable of reading an audio file or a transcript and converting it into a sequence of sign language movements. A 3D or 2D avatar animation may be generated from the sequence of sign language movements in a primary window or in a secondary window on a user's computing device (or in a virtual reality or augmented reality space) using accelerated graphical APIs To make movements appear more natural, the sequence of gestures/movements may be generated through an AI model, or a combination of natural language analysis and an AI model, to smooth out any possible transition across the gestures and adapt it to the viewing condition.

Abstract: A system and/or method for efficiently playing a multitude of short looping videos at the same time on a client's browser screen (or live event interface) by leveraging the GPU capabilities instead of relying on traditional video/GIF playback methods. In a specific embodiment the looping videos are implemented as GPU compressed textures (e.g.

Abstract: An aspect of the subject technology includes a method including receiving a request including an input file and a selection. The input file is in the fragmented ISOBMFF format. The method also includes parsing one or more fragments from the input file, generating a cache object based on the fragments, generating an output moov box based on at least one of the fragments or the cache object, calculating output mdat offsets for the selection corresponding to the fragments based on at least one of the fragments or the cache object, and determining output bytes of an output mdat section. The output mdat section is based on the output mdat offsets. The method further includes multiplexing the output bytes to the progressive ISOBMFF format, and serving the multiplexed output bytes.

Abstract: Methods, systems, and computer program products for classifying a spatial format of a video file. The system includes one or more processors and a memory coupled to the processors. The memory stores data comprising program code that, when executed by the processors, causes the system to allow video sharing platforms to support multiple formats without asking users to manually identify the format of the video.