Methods and systems for encoder parameter setting optimization

A system comprising:

• a memory device;
• and
• a set of one or more processors coupled to the memory device, the set of one or more processors to perform operations comprising:
• identifying a media item to be provided to one or more users of a platform, wherein the identified media item is associated with a media class of a plurality of media classes;
• providing an indication of the identified media item as input to a machine learning model trained based on historical encoding data to predict, for a given media item, a set of encoder parameter settings that satisfy a performance criterion in view of a respective media class associated with the given media item, the historical encoding data comprising a prior set of encoder parameter settings that satisfied the performance criterion with respect to a prior media item associated with the respective media class;
• obtaining one or more outputs of the machine learning model, wherein the one or more outputs identify:
• one or more sets of encoder parameter settings and, for each of the one or more sets of encoder parameter settings, and
• for each of the one or more sets of the encoder parameter settings, a level of confidence that a respective set of encoder parameter settings satisfies the performance criterion in view of the media class associated with the media item;
• identifying, based on one or more outputs of the machine learning model, at least one respective set of encoder parameter settings having a level of confidence that satisfies a confidence criterion, wherein the level of confidence indicates whether the set of encoder parameter settings satisfies the performance criterion in view of the media class associated with the identified media item;
• and
• causing the identified media item to be encoded using the identified at least one respective set of encoder parameter settings.

A non-transitory computer readable storage medium comprising instructions for a server that, when executed by a set of one or more processors, cause the set of one or more processors to perform operations comprising:

• identifying a media item to be provided to one or more users of a platform, wherein the identified media item is associated with a media class of a plurality of media classes;
• providing an indication of the identified media item as input to a machine learning model trained based on historical encoding data to predict, for a given media item, a set of encoder parameter settings that satisfy a performance criterion in view of a respective media class associated with the given media item, the historical encoding data comprising a prior set of encoder parameter settings that satisfied the performance criterion with respect to a prior media item associated with the respective media class;
• obtaining one or more outputs of the machine learning model, wherein the one or more outputs identify:
• one or more sets of encoder parameter settings, and
• for each of the one or more sets of the encoder parameter settings, a level of confidence that a respective set of encoder parameter settings satisfies the performance criterion in view of the media class associated with the identified media item;
• identifying, based on one or more outputs of the machine learning model, at least one respective set of encoder parameter settings having a level of confidence that satisfies a confidence criterion, wherein the level of confidence indicates whether the set of encoder parameter settings satisfies the performance criterion in view of the media class associated with the identified media item;
• and
• causing the identified media item to be encoded using the identified respective set of encoder parameter settings.