this post was submitted on 25 Nov 2023
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What would you use to optimally store videos ? (alien.top)
submitted 1 year ago by jyroman53@alien.top to c/main@selfhosted.forum
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Hello Everyone,

I'm currently looking for a way to optimise even further the amount of video file I have, I mainly store anime and series episodes.

Currently what I do is : Getting the video on my main computer (5800X3D + 1080ti) Convert it using Handbrake to H265 NVENC then store it on my server which is basically a Ubuntu server (i7 8700 no graphic cards) with mainly a zfs pool.

I'm looking to improve this to getting directly the video on my server, converting it using handbrake (I'm considering getting an intel ark card to switch to AV1 instead of H265) and storing it directly on it.

The issues I'm currently facing are mainly concerning Handbrake itself, the support on linux using hardware encoding looks limited I still have to make more testing but I was wondering if anyone got a better solution than what I'm currently envisaging.

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[–] ColonelRyzen@alien.top 1 points 1 year ago

I've been debating this as well and I decided to go with H.255 since AV1 decoding hardware is years out for any client devices that access Plex. I am going to use Handbrake CLI in Linux to run the conversion of all of my media for space savings. I am using the Super HQ present with audio passthrough for all resolution ranges (except 4K).

I have about 18TB of media to churn though and I don't want it taking a all winter or tying up my desktop/server for that duration. My plan is to distribute the transcoding tasks across a SLURM compute cluster I built out of old Dell desktops. Need to do the math on how long it might take, but I have a 8 thread/8GB VMs living on all PCs in the house as nodes for the cluster which totals 14. I wrote a couple python scripts to create the transcode jobs, run them and handle file movement.

I'm thinking I will see about 50% storage savings on all transcoded media. If you want more details or the scripts, let me know and I will put them on GitHub.