Abstract: Techniques for video encoding are described herein. A method for video encoding with smart chunking includes receiving, by a distributed video encoding system, a video input and a target bitrate, the video input having segments of a segment duration, determining an internal chunk length that is a multiple of the segment duration, encoding chunks having the internal chunk length, wherein the average bitrate across the chunk is equal to the target bitrate, and segmenting the encoded chunks into encoded segments of the segment duration. The distributed video encoding system may include various video encoders, or encoder instances, able to encode multiple chunks in parallel. The encoded segments may be output to a client, all of the encoded segments being of equal or similar quality.

Abstract: An original input video file is encoded using a machine learning approach. The encoder performs a detailed video analysis and selection of encoding parameters that using a machine learning algorithm improves over time. The encoding process is done using a multi-pass approach. During a first pass, the entire video file is scanned to extract video property information that does not require in-depth analyses. The extracted data is then entered into an encoding engine, which uses artificial intelligence to produce optimized encoder settings. The video file is into a set of time-based chunks and, in a second pass, the encoding parameters for each chunk are set and distributed to encoding nodes for parallel processing. These encoder instances probe-encode each chunk determine the level of complexity for the chunk and to derive chunk-specific encoding parameters.

Abstract: An original input video file is encoded using a machine learning approach. The encoder performs a detailed video analysis and selection of encoding parameters that using a machine learning algorithm improves over time. The encoding process is done using a multi-pass approach. During a first pass, the entire video file is scanned to extract video property information that does not require in-depth analyses. The extracted data is then entered into an encoding engine, which uses artificial intelligence to produce optimized encoder settings. The video file is into a set of time-based chunks and, in a second pass, the encoding parameters for each chunk are set and distributed to encoding nodes for parallel processing. These encoder instances probe-encode each chunk determine the level of complexity for the chunk and to derive chunk-specific encoding parameters.

Abstract: An original input video file is encoded using a machine learning approach. The encoder performs a detailed video analysis and selection of encoding parameters that using a machine learning algorithm improves over time. The encoding process is done using a multi-pass approach. During a first pass, the entire video file is scanned to extract video property information that does not require in-depth analyses. The extracted data is then entered into an encoding engine, which uses artificial intelligence to produce optimized encoder settings. The video file is into a set of time-based chunks and, in a second pass, the encoding parameters for each chunk are set and distributed to encoding nodes for parallel processing. These encoder instances probe-encode each chunk determine the level of complexity for the chunk and to derive chunk-specific encoding parameters.

Abstract: A video streaming system optimizes the buffering of periods of frames of a video presentation in order to achieve a more constant perceptual quality throughout the entire video presentation. An adaption algorithm determines transmission bitrates to transmit some periods at a lower bitrate that the channel conditions of the channel may allow while transmitting other periods at a higher bitrate. The transmission bitrates are determined based on expected quality metadata signaled in the periods of the bitstream for the current period and following periods in order to optimize the bitrate and the expected perceptual quality of each version of each period over time.

Abstract: An original input video file is encoded using a machine learning approach. The encoder performs a detailed video analysis and selection of encoding parameters that using a machine learning algorithm improves over time. The encoding process is done using a multi-pass approach. During a first pass, the entire video file is scanned to extract video property information that does not require in-depth analyses. The extracted data is then entered into an encoding engine, which uses artificial intelligence to produce optimized encoder settings. The video file is into a set of time-based chunks and, in a second pass, the encoding parameters for each chunk are set and distributed to encoding nodes for parallel processing. These encoder instances probe-encode each chunk determine the level of complexity for the chunk and to derive chunk-specific encoding parameters.