Bitmovin Gmbh

Martin Smole

Armin Trattnig

Christian Feldmann

Christian Timmerer

Minh Nguyen

Daniele Lorenzi

Farzad Tashtarian

Roland Kersche

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.

Energy-aware ABR algorithm for green HTTP adaptive video streaming

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.

Video encoding based on customized bitrate table

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. Following completion of the second pass, the results of both passes are then merged to obtain the necessary information for the encoder to achieve the best possible result.

Optimized multipass encoding

Growing community of inventors

Klagenfurt, Austria

Martin Smole