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• How to create a DASH VOD for Chromecast with ffmpeg ?

  19 novembre 2020, par Oleg Yablokov

  I need to serve long videos ( 2 hours) from a web server to mobile clients and the clients should be able to play the videos via Chromecast. I have chosen mpeg-dash for this purpose : video encoder is h.264 (level 4.1), audio is aac (although I've tried diffrent ones).

  


  I've tried ffmpeg, MP4Box and some other tools to generate videos ; most of the time I succeeded playing them on VLC or on a mobile client (locally), but not with Chromecast.

  


  I've tried Amazon's Elastic Transcoder and it worked, but it gave me one big file whereas I need many small segments.

  


  CORS are set.

  


  Chromecast remote debugging didn't help much.

  


  Do you know how to do this ?

  


• AWS Lambda execution time for FFMPEG transcoding

  4 janvier 2023, par FlamingMoe

  I'm using AWS Lambda for converting files from WEBM to MP4

  


  I'm using ffmpeg version 4.3.1-static https://johnvansickle.com/ffmpeg/ (I have done the following tests also with the ffmpeg in serverless AWS ffmpeg layer (that includes de 4.1.3), but results are even worse (about 25% slower)

  


  I'm using Node 10x as container.

  


  WEBM size   Time to convert.  Memory Lambda.  Memory used (as shown in log)

80Mb             ~44s              3008            410
40Mb             ~44s              3008            375

80Mb             ~70s              1024            321
40Mb             ~70s              1024            279


  


  All videos are 80s length. So as far as I can see, it does not matter the size of the WEBM, if the length of the video is the same, it takes the same to convert. So ffmpeg takes more time if the video length is higher, not if the file size is higher ... curious ;-)

  


  But in the other hand, I'm confused with Lambda memory. I know memory and CPU comes together in Lambda ... the more memory you choose, the more CPU is assigned.

  


  But...

  


  

  1. Why ffmpeg just take about 300/400Mb if it has more to run ?

  


  2. How can I tell ffmpeg to use more memory ?

  


  3. Is there any option to accelerate the process in Lambda ?

  


  


  Btw, In all tests, all ffmpeg are the same, and

  


  cpu-used paramenter)

  


  

  • I added to ffmpeg parameters cpu-used=100, and it does not matter at all if I put cpu-used=5 ... times are the same, so I guess that parameter is useless (i don't know why)

  


  


  threads parameter)

  


  

  • Also I did some tests with "threads" parameters, but it's useless also.

  


  


  I know it's not a good comparison, but same files takes about 5 seconds to be converted in a simple dedicated server (8 vCores and 8GB RAM in OVH Centos VPS).

  


  Btw, Amazon Elastic Transcoder is not an option :
a) it's extremely more expensive
b) it has just his profiles to convert, and my ffmpeg commands are very complex (watermarks, effects, etc ...)

  


• Generating number of thumbnails depending on video size using AWS MediaConvert

  1er décembre 2020, par sakhunzai

  After reading this article I get the sense that AWS media convert job template cannot be re-used to generate thumbnails of arbitrary video size. The article assumes that we know the size/duration of video uploaded upfront hence the number of thumbnails we desire.

  


  What I am looking for is to generate a random number of thumbnails based of video size (e.g large number of thumbnails for large video and small number of thumbnails for small video). I approached this solution using lambda trigger and ffmpeg lambda layer but lambda function timeouts (15 minutes max) for videos larger than 150MB (since it takes time to read the video from the s3 bucket).

  


  What are my options to process large number of video , generate a variable number of thumbnails, merge those thumbs to generate a sprite ?

  


  I tried lambda trigger with ffmpeg/ffprob to generate sprite, but that has timeout issue. Now I have setup a cloud watch event rule to trigger lambda function on mediaconvert job status change( completed)
and merge the thumbs to generate sprite, which seems much lighter but I need an arbitrary number of thumbs.