this post was submitted on 21 Jun 2023
127 points (100.0% liked)
Technology
37794 readers
272 users here now
A nice place to discuss rumors, happenings, innovations, and challenges in the technology sphere. We also welcome discussions on the intersections of technology and society. If it’s technological news or discussion of technology, it probably belongs here.
Remember the overriding ethos on Beehaw: Be(e) Nice. Each user you encounter here is a person, and should be treated with kindness (even if they’re wrong, or use a Linux distro you don’t like). Personal attacks will not be tolerated.
Subcommunities on Beehaw:
This community's icon was made by Aaron Schneider, under the CC-BY-NC-SA 4.0 license.
founded 2 years ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
Plus it's computationally expensive. YouTube has entire data centers filled with servers using custom silicon to encode ingested videos into nearly every resolution/framerate and codec they serve, so that different clients get the most efficient option for their quality settings and supported codecs, no matter what the original uploader happened to upload. Granted, that workflow mainly makes sense because of bandwidth costs, but the high quality of the user experience depends on that backend.
As I understand it, it ingests an uploaded video and automatically encodes it in a bunch of different quality settings in h.264, then, if the video is popular enough to justify the computational cost of encoding into AV1 and VP9, they'll do that when the video reaches something like 1000 views. And yes, once encoded they just keep the copies so that it doesn't have to be done again.
Here's a 2-year-old blog post where YouTube describes some of the technical challenges.
As that blog post explains, when you're running a service that ingests 500 hours of user submitted video every minute, you'll need to handle that task differently than how, for example, Netflix does (with way more video minutes being served, but a comparatively tiny amount of original video content to encode, where bandwidth efficiency becomes far more important than encoding computational efficiency).