Netflix Inc.

Jan De Cock

Anne Aaron

Ioannis Katsavounidis

Liwei Guo

Kyle Christopher Swanson

Glenn Van Wallendael

Sonia Bhaskar

In various embodiments, a sequence-based encoding application partitions a set of shot sequences associated with a media title into multiple clusters based on at least one feature that characterizes media content and/or encoded media content associated with the media title. The clusters include at least a first cluster and a second cluster. The sequence-based encoding application encodes a first shot sequence using a first operating point to generate a first encoded shot sequence. The first shot sequence and the first operating point are associated with the first cluster. By contrast, the sequence-based encoding application encodes a second shot sequence using a second operating point to generate a second encoded shot sequence. The second shot sequence and the second operating point are associated with the second cluster. Subsequently, the sequence-based encoding application generates an encoded media sequence based on the first encoded shot sequence and the second encoded shot sequence.

Optimizing encoding operations when generating encoded versions of a media title

In various embodiments, an iterative encoding application generates shot encode points based on a first set of encoding points and a first shot sequence associated with a media title. The iterative encoding application performs convex hull operations across the shot encode points to generate a first convex hull. Subsequently, the iterative encoding application generates encoded media sequences based on the first convex hull and a second convex hull that is associated with both a second shot sequence associated with the media title and a second set of encoding points. The iterative encoding application determines a first optimized encoded media and a second optimized encoded media sequence from the encoded media sequences based on, respectively, a first target metric value and a second target metric value for a media metric. Portions of the optimized encoded media sequences are subsequently streamed to endpoint devices during playback of the media title.

Iterative techniques for generating multiple encoded versions of a media title

In various embodiments, an interpolation-based encoding application encodes a first subsequence included in a media title at each encoding point included in a first set of encoding points to generate encoded subsequences. Subsequently, the interpolation-based encoding application performs interpolation operation(s) based on the encoded subsequences to estimate a first media metric value associated with a first encoding point that is not included in the first set of encoding points. The interpolation-based encoding application then generates an encoding recipe based on the encoded subsequences and the first media metric value. The encoding recipe specifies a different encoding point for each subsequence included in the media title. After determining that the encoding recipe specifies the first encoding point for the first subsequence, the interpolation-based encoding application encodes the first subsequence at the first encoding point to generate at least a portion of an encoded version of the media title.

Techniques for efficiently performing subsequence-based encoding for a media title

A bitrate allocation engine allocates bitrates for distributed encoding of source data. Upon receiving a chunk of source data, the bitrate allocation engine generates a curve based on multiple points that each specify a different visual quality level and corresponding encoding bitrate for encoding the chunk. Subsequently, the bitrate allocation engine computes an optimized encoding bitrate based on the generated curve and an optimization factor that is associated with different visual quality levels and corresponding encoding bitrates for multiple chunks of the source data. The bitrate allocation engine then causes the chunk to be encoded at the optimized encoding bitrate. Advantageously, the resulting encoded chunk is optimized with respect to the optimization factor for multiple chunks of the source data.

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Cupertino, CA, United States of America

Jan De Cock