Abstract: The technology described herein relates to fast hybrid per-title encoding for video streaming. A method includes performing a probe encoding on representations of a video input using a low complex encoder, generating a bitrate table using probe results from the probe encoding, computing a bitrate ladder, by a per-title algorithm, using the bitrate table, and encoding the video input using the bitrate ladder and a different encoder. The method may include, prior to performing the probe encoding, determining that the video input exceeds a threshold length, trimming the video input into trimmings of a given trimmed length, and combining a given number (i.e., subset) of trimmings into a shortened video input, wherein the probe encoding is performed on the shortened video input instead of the video input in full.

Abstract: Techniques for implementing an energy-aware ABR algorithm for adaptive streaming may include determining whether a buffer level is less than a threshold buffer, selecting a lowest bitrate representation for playback of a segment of a video in a conservative mode when the buffer level is less than the threshold buffer, calculating a cost of a representation in an operative mode when the buffer level exceeds the threshold buffer, the cost of the representation comprising a weighted sum of a throughput cost, a buffer cost, a quality cost, and optionally also an energy cost, selecting a bitrate for a next segment of the video based on the cost of the representation, and providing to a client device a selected representation. The energy-aware ABR algorithm may be implemented when an ECO mode is selected in a client device.

Abstract: The technology described herein relates to hybrid three pass encoding for video streaming. A method for hybrid three pass encoding may include performing a first pass encoding of a video input using a lower complexity encoder, splitting the video input into segments, performing a two pass encoding of each of the segments using a higher complexity encoder and the complexity curve generated in the first pass encoding, and outputting an encoded version of the video input. The first pass using a lower complexity encoder significantly reduces the encoding time and end-to-end encoding complexity. In some embodiments, the first pass may be performed on one of many renditions of the video input, the resulting complexity curve being used for subsequent two pass encodings of many or all renditions of the video input.

Abstract: An original input content is subjected to multiple constant quality probe encodes for a defined set of resolutions. In one embodiment, probe encodes encode a few parts of the original source video, for example, 30 seconds from 5 different positions. Each probe encode delivers an average bitrate that is required to achieve the configured constant quality. The mean value of the average bitrate is taken per resolution. This results in a list of bitrates that map to a resolution that would achieve the best quality, a custom bitrate table. Based on the custom bitrate table, an optimized bitrate ladder is computed. The process starts with a configurable minimum bitrate and steps up by a bitrate step size that is between a configurable min and max bitrate step size until the bitrate of the highest resolution is reached.

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 content is subjected to multiple constant quality probe encodes for a defined set of resolutions. In one embodiment, probe encodes encode a few parts of the original source video, for example, 30 seconds from 5 different positions. Each probe encode delivers an average bitrate that is required to achieve the configured constant quality. The mean value of the average bitrate is taken per resolution. This results in a list of bitrates that map to a resolution that would achieve the best quality, a custom bitrate: table. Based on the custom bitrate table, an optimized bitrate ladder is computed. The process starts with a configurable minimum bitrate and steps up by a bitrate step size that is between a configurable min and max bitrate step size until the bitrate of the highest resolution is reached.

Abstract: An original input content is subjected to multiple constant quality probe encodes for a defined set of resolutions. In one embodiment, probe encodes encode a few parts of the original source video, for example, 30 seconds from 5 different positions. Each probe encode delivers an average bitrate that is required to achieve the configured constant quality. The mean value of the average bitrate is taken per resolution. This results in a list of bitrates that map to a resolution that would achieve the best quality, a custom bitrate table. Based on the custom bitrate table, an optimized bitrate ladder is computed. The process starts with a configurable minimum bitrate and steps up by a bitrate step size that is between a configurable min and max bitrate step size until the bitrate of the highest resolution is reached.

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.