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• ffmpeg streaming fails to stream over internet to twitch.tv

  15 avril 2021, par josh joyer

  I did already streaming to twitch.tv with command :

  


  ffmpeg -stream_loop -1 -i 9stream.wav 
-f dshow -i audio="mic"
 -f dshow -i audio="realTek" 
-filter_complex "[0:a]volume=2[a0];[1:a]volume=1.5[a1];[2:a]volume=1.5[a2];[a0][a1][a2]amix=inputs=3"
 -f dshow -i video="USB2.0 PC CAMERA" 
-ac 1 -ar 11025 -acodec libmp3lame -c:v libx264 -b:v 100k -f flv -s 80x120 
rtmp://live.twitch.tv/app/live_streamingKey


  


  It was most advanced command that I used to stream online.

  


  (I do not know how to make enter in here so I put double enter)

  


  9stream.wav was played in loop as background music

  


  microphone was added

  


  stereoMix named realTek was the playback of system sounds

  


  volume was adjusted and all sounds mixed into one stream

  


  camera view was added

  


  THEN network flow was reduced by sending only one channel with low frequency of 11025 with lowest

  


  possible data size made by mp3 encoder and libx264 was used to encode video in png files.

  


  It was working fine SO I decided to make final version

  


  (this one worked with all sounds(background music,microphone,system sounds) and camera)

  


  Final version was about adding screen view and logo.

  


  I succeded writing everything to disc with command :

  


  ffmpeg 
-stream_loop -1 -i 9stream.wav 
-f dshow -i audio="mic" 
-f dshow -i audio="stereoMixRealtek" 
-i camera.png 
-f gdigrab -framerate 1 -i desktop 
-f dshow -framerate 15 -i video="USB2.0 PC CAMERA" 
-filter_complex "[0:a]volume=2[a0];[1:a]volume=1.5[a1];[2:a]volume=1.5[a2];
[a0][a1][a2]amix=inputs=3[aMix];
[4:v]scale=200:-1[v4];[5:v]scale=50:-1[v5];
[v4][v5]overlay=(W-w)-5:(H-h)-5[vScreenCam];
[vScreenCam][3:v]overlay=5:5[v]" 
-map "[v]" -map "[aMix]" -ac 1 -ar 11025 -c:a libmp3lame -r 1 -c:v libx264 output.mkv


  


  That was

  


  background music

  


  microphone

  


  system sounds

  


  logo picture

  


  screen view

  


  camera

  


  adjusting sound volume

  


  mixing sounds

  


  reducing size of screen view and camera view

  


  overlaying reduced camera view over reduced screen view

  


  adding logo

  


  choosing final view, final mixed sounds,

  


  reducing data size to one channel, reducing sample frequency,

  


  choosing mp3 codec to reduce final data size,

  


  choosing minimal framerate of one per second to reduce data size

  


  choosing libx264 codec for video.

  


  THEN I tried to use final command for network streaming with slight modification :

  


  ffmpeg 
-stream_loop -1 -i 9stream.wav 
-f dshow -i audio="mic" 
-f dshow -i audio="stereo mix" 
-i camera.png 
-f gdigrab -framerate 1 -i desktop 
-f dshow -framerate 15 -i video="USB2.0 PC CAMERA" 
-filter_complex "[0:a]volume=2[a0];[1:a]volume=1.5[a1];[2:a]volume=1.5[a2];
[a0][a1][a2]amix=inputs=3[aMix];
[4:v]scale=200:-1[v4];[5:v]scale=50:-1[v5];
[v4][v5]overlay=(W-w)-5:(H-h)-5[vScreenCam];[vScreenCam][3:v]overlay=5:5[v]" 
-map "[v]" -map "[aMix]" 
-ac 1 -ar 11025 -c:a libmp3lame -r 1 -c:v libx264 -b:v 100k -b:a 10k -f flv rtmp://live.twitch.tv/app/live_streamingKey


  


  I added parameter
-b:v 100k to reduce video flow
-b:a 10k to reduce sound flow
-f flv to be good for twitch.tv otherwise it would not accept stream

  


  BUT ffmpeg is always stopping sending data with message like this :

  


  testosteron_@testosteron MINGW64 ~/Desktop/2021b/magisterka/ScreenRecorderXi/ScreenRecorderXi/bin
$ cmd
Microsoft Windows [Version 6.3.9600]
(c) 2013 Microsoft Corporation. Wszelkie prawa zastrze▒one.

C:\Users\testosteron_\Desktop\2021b\magisterka\ScreenRecorderXi\ScreenRecorderXi\bin>ffmpeg -stream_loop -1 -i 9stream.wav -f dshow -i audio="@device_cm_{33D9A762-90C8-11D0-BD43-00A0C911CE86}\wave_{5B4DB0B5-B645-4AFA-930D-4710AAF753DB}" -f dshow -i audio="@device_cm_{33D9A762-90C8-11D0-BD43-00A0C911CE86}\wave_{ADECEC1D-C3CC-4BAE-8516-752251B8B63F}" -i camera.png -f gdigrab -framerate 1 -i desktop -f dshow -framerate 15 -i video="USB2.0 PC CAMERA" -filter_complex "[0:a]volume=2[a0];[1:a]volume=1.5[a1];[2:a]volume=1.5[a2];[a0][a1][a2]amix=inputs=3[aMix];[4:v]scale=200:-1[v4];[5:v]scale=50:-1[v5];[v4][v5]overlay=(W-w)-5:(H-h)-5[vScreenCam];[vScreenCam][3:v]overlay=5:5[v]" -map "[v]" -map "[aMix]" -ac 1 -ar 11025 -c:a libmp3lame -r 1 -c:v libx264 -b:v 100k -b:a 10k -f flv rtmp://live.twitch.tv/app/live_674912043_oAwGnACTndHyeZnlA6scLegm8gaxwf
ffmpeg -stream_loop -1 -i 9stream.wav -f dshow -i audio="@device_cm_{33D9A762-90C8-11D0-BD43-00A0C911CE86}\wave_{5B4DB0B5-B645-4AFA-930D-4710AAF753DB}" -f dshow -i audio="@device_cm_{33D9A762-90C8-11D0-BD43-00A0C911CE86}\wave_{ADECEC1D-C3CC-4BAE-8516-752251B8B63F}" -i camera.png -f gdigrab -framerate 1 -i desktop -f dshow -framerate 15 -i video="USB2.0 PC CAMERA" -filter_complex "[0:a]volume=2[a0];[1:a]volume=1.5[a1];[2:a]volume=1.5[a2];[a0][a1][a2]amix=inputs=3[aMix];[4:v]scale=200:-1[v4];[5:v]scale=50:-1[v5];[v4][v5]overlay=(W-w)-5:(H-h)-5[vScreenCam];[vScreenCam][3:v]overlay=5:5[v]" -map "[v]" -map "[aMix]" -ac 1 -ar 11025 -c:a libmp3lame -r 1 -c:v libx264 -b:v 100k -b:a 10k -f flv rtmp://live.twitch.tv/app/live_674912043_oAwGnACTndHyeZnlA6scLegm8gaxwf
ffmpeg version git-2020-08-02-b48397e Copyright (c) 2000-2020 the FFmpeg developers
  built with gcc 10.2.1 (GCC) 20200726
  configuration: --enable-gpl --enable-version3 --enable-sdl2 --enable-fontconfig --enable-gnutls --enable-iconv --enable-libass --enable-libdav1d --enable-libbluray --enable-libfreetype --enable-libmp3lame --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenjpeg --enable-libopus --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libsrt --enable-libtheora --enable-libtwolame --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxml2 --enable-libzimg --enable-lzma --enable-zlib --enable-gmp --enable-libvidstab --enable-libvmaf --enable-libvorbis --enable-libvo-amrwbenc --enable-libmysofa --enable-libspeex --enable-libxvid --enable-libaom --enable-libgsm --enable-librav1e --disable-w32threads --enable-libmfx --enable-ffnvcodec --enable-cuda-llvm --enable-cuvid --enable-d3d11va --enable-nvenc --enable-nvdec --enable-dxva2 --enable-avisynth --enable-libopenmpt --enable-amf
  libavutil      56. 57.100 / 56. 57.100
  libavcodec     58. 99.100 / 58. 99.100
  libavformat    58. 49.100 / 58. 49.100
  libavdevice    58. 11.101 / 58. 11.101
  libavfilter     7. 87.100 /  7. 87.100
  libswscale      5.  8.100 /  5.  8.100
  libswresample   3.  8.100 /  3.  8.100
  libpostproc    55.  8.100 / 55.  8.100
Guessed Channel Layout for Input Stream #0.0 : stereo
Input #0, wav, from '9stream.wav':
  Metadata:
    encoder         : Lavf58.49.100
  Duration: 00:00:13.48, bitrate: 1411 kb/s
    Stream #0:0: Audio: pcm_s16le ([1][0][0][0] / 0x0001), 44100 Hz, stereo, s16, 1411 kb/s
Guessed Channel Layout for Input Stream #1.0 : stereo
Input #1, dshow, from 'audio=@device_cm_{33D9A762-90C8-11D0-BD43-00A0C911CE86}\wave_{5B4DB0B5-B645-4AFA-930D-4710AAF753DB}':
  Duration: N/A, start: 209609.948000, bitrate: 1411 kb/s
    Stream #1:0: Audio: pcm_s16le, 44100 Hz, stereo, s16, 1411 kb/s
Guessed Channel Layout for Input Stream #2.0 : stereo
Input #2, dshow, from 'audio=@device_cm_{33D9A762-90C8-11D0-BD43-00A0C911CE86}\wave_{ADECEC1D-C3CC-4BAE-8516-752251B8B63F}':
  Duration: N/A, start: 209610.502000, bitrate: 1411 kb/s
    Stream #2:0: Audio: pcm_s16le, 44100 Hz, stereo, s16, 1411 kb/s
Input #3, png_pipe, from 'camera.png':
  Duration: N/A, bitrate: N/A
    Stream #3:0: Video: png, rgba(pc), 32x32 [SAR 3779:3779 DAR 1:1], 25 tbr, 25 tbn, 25 tbc
[gdigrab @ 0000009a3f019700] Capturing whole desktop as 1280x1024x32 at (0,0)
[gdigrab @ 0000009a3f019700] Stream #0: not enough frames to estimate rate; consider increasing probesize
Input #4, gdigrab, from 'desktop':
  Duration: N/A, start: 1618506176.140738, bitrate: 41943 kb/s
    Stream #4:0: Video: bmp, bgra, 1280x1024, 41943 kb/s, 1 fps, 1000k tbr, 1000k tbn, 1000k tbc
Input #5, dshow, from 'video=USB2.0 PC CAMERA':
  Duration: N/A, start: 209613.583000, bitrate: N/A
    Stream #5:0: Video: rawvideo (YUY2 / 0x32595559), yuyv422, 640x480, 15 fps, 15 tbr, 10000k tbn, 10000k tbc
[dshow @ 0000009a3f034900] real-time buffer [USB2.0 PC CAMERA] [video input] too full or near too full (101% of size: 3041280 [rtbufsize parameter])! frame dropped!
    Last message repeated 9 times
Stream mapping:
  Stream #0:0 (pcm_s16le) -> volume
  Stream #1:0 (pcm_s16le) -> volume
  Stream #2:0 (pcm_s16le) -> volume
  Stream #3:0 (png) -> overlay:overlay
  Stream #4:0 (bmp) -> scale
  Stream #5:0 (rawvideo) -> scale
  overlay -> Stream #0:0 (libx264)
  amix -> Stream #0:1 (libmp3lame)
Press [q] to stop, [?] for help
[dshow @ 0000009a3efd5b80] Thread message queue blocking; consider raising the thread_queue_size option (current value: 8)
[dshow @ 0000009a406fb280] Thread message queue blocking; consider raising the thread_queue_size option (current value: 8)
[libx264 @ 0000009a4082ddc0] using cpu capabilities: MMX2 SSE2Fast SSSE3 Cache64 SlowShuffle
[libx264 @ 0000009a4082ddc0] profile High, level 1.1, 4:2:0, 8-bit
[libx264 @ 0000009a4082ddc0] 264 - core 161 - H.264/MPEG-4 AVC codec - Copyleft 2003-2020 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=5 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=abr mbtree=1 bitrate=100 ratetol=1.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00
Output #0, flv, to 'rtmp://live.twitch.tv/app/live_streamingKey':
  Metadata:
    encoder         : Lavf58.49.100
    Stream #0:0: Video: h264 (libx264) ([7][0][0][0] / 0x0007), yuv420p(progressive), 200x160, q=-1--1, 100 kb/s, 1 fps, 1k tbn, 1 tbc (default)
    Metadata:
      encoder         : Lavc58.99.100 libx264
    Side data:
      cpb: bitrate max/min/avg: 0/0/100000 buffer size: 0 vbv_delay: N/A
    Stream #0:1: Audio: mp3 (libmp3lame) ([2][0][0][0] / 0x0002), 11025 Hz, mono, fltp, 10 kb/s (default)
    Metadata:
      encoder         : Lavc58.99.100 libmp3lame
frame=    1 fps=0.0 q=0.0 size=       0kB time=00:00:00.00 bitrate=N/A speed=   frame=    1 fps=1.0 q=0.0 size=       0kB time=00:00:00.00 bitrate=N/A speed=   frame=    1 fps=0.7 q=0.0 size=       0kB time=00:00:00.00 bitrate=N/A speed=   frame=    3 fps=1.5 q=0.0 size=       0kB time=00:00:03.08 bitrate=   1.0kbits/sframe=    4 fps=1.6 q=0.0 size=       0kB time=00:00:03.66 bitrate=   0.8kbits/sframe=    4 fps=1.3 q=0.0 size=       0kB time=00:00:03.66 bitrate=   0.8kbits/sframe=    5 fps=1.4 q=0.0 size=       0kB time=00:00:04.65 bitrate=   0.7kbits/sframe=    5 fps=1.2 q=0.0 size=       0kB time=00:00:04.65 bitrate=   0.7kbits/sframe=    6 fps=1.3 q=0.0 size=       0kB time=00:00:05.64 bitrate=   0.5kbits/sframe=    6 fps=1.2 q=0.0 size=       0kB time=00:00:05.64 bitrate=   0.5kbits/sframe=    7 fps=1.3 q=0.0 size=       0kB time=00:00:06.64 bitrate=   0.5kbits/sframe=    7 fps=1.2 q=0.0 size=       0kB time=00:00:06.64 bitrate=   0.5kbits/sframe=    8 fps=1.2 q=0.0 size=       0kB time=00:00:07.58 bitrate=   0.4kbits/sframe=    8 fps=1.1 q=0.0 size=       0kB time=00:00:07.58 bitrate=   0.4kbits/sframe=    9 fps=1.2 q=0.0 size=       0kB time=00:00:08.57 bitrate=   0.4kbits/sframe=    9 fps=1.1 q=0.0 size=       0kB time=00:00:08.57 bitrate=   0.4kbits/sframe=   10 fps=1.2 q=0.0 size=       0kB time=00:00:09.56 bitrate=   0.3kbits/sframe=   10 fps=1.1 q=0.0 size=       0kB time=00:00:09.56 bitrate=   0.3kbits/sframe=   11 fps=1.1 q=0.0 size=       1kB time=00:00:10.55 bitrate=   0.9kbits/sframe=   11 fps=1.1 q=0.0 size=       1kB time=00:00:10.55 bitrate=   0.9kbits/sframe=   12 fps=1.1 q=0.0 size=       2kB time=00:00:11.55 bitrate=   1.7kbits/sframe=   12 fps=1.1 q=0.0 size=       2kB time=00:00:11.55 bitrate=   1.7kbits/sframe=   13 fps=1.1 q=0.0 size=       4kB time=00:00:12.59 bitrate=   2.5kbits/sframe=   13 fps=1.1 q=0.0 size=       4kB time=00:00:12.59 bitrate=   2.5kbits/sframe=   14 fps=1.1 q=0.0 size=       5kB time=00:00:13.58 bitrate=   3.0kbits/sframe=   14 fps=1.1 q=0.0 size=       5kB time=00:00:13.58 bitrate=   3.0kbits/sframe=   15 fps=1.1 q=0.0 size=       6kB time=00:00:14.58 bitrate=   3.5kbits/sframe=   15 fps=1.1 q=0.0 size=       6kB time=00:00:14.58 bitrate=   3.5kbits/sframe=   16 fps=1.1 q=0.0 size=       8kB time=00:00:15.57 bitrate=   4.0kbits/sframe=   16 fps=1.1 q=0.0 size=       8kB time=00:00:15.57 bitrate=   4.0kbits/sframe=   17 fps=1.1 q=0.0 size=       9kB time=00:00:16.56 bitrate=   4.4kbits/sframe=   17 fps=1.1 q=0.0 size=       9kB time=00:00:16.56 bitrate=   4.4kbits/sframe=   18 fps=1.1 q=0.0 size=      10kB time=00:00:17.55 bitrate=   4.7kbits/sframe=   18 fps=1.0 q=0.0 size=      10kB time=00:00:17.55 bitrate=   4.7kbits/sframe=   19 fps=1.1 q=0.0 size=      11kB time=00:00:18.55 bitrate=   5.0kbits/sframe=   19 fps=1.0 q=0.0 size=      11kB time=00:00:18.55 bitrate=   5.0kbits/sframe=   20 fps=1.1 q=0.0 size=      13kB time=00:00:19.54 bitrate=   5.3kbits/sframe=   20 fps=1.0 q=0.0 size=      13kB time=00:00:19.54 bitrate=   5.3kbits/sframe=   21 fps=1.1 q=0.0 size=      14kB time=00:00:20.58 bitrate=   5.6kbits/sframe=   21 fps=1.0 q=0.0 size=      14kB time=00:00:20.58 bitrate=   5.6kbits/sframe=   22 fps=1.1 q=0.0 size=      15kB time=00:00:21.58 bitrate=   5.8kbits/sframe=   22 fps=1.0 q=0.0 size=      15kB time=00:00:21.58 bitrate=   5.8kbits/sframe=   23 fps=1.1 q=0.0 size=      17kB time=00:00:22.57 bitrate=   6.0kbits/sframe=   23 fps=1.0 q=0.0 size=      17kB time=00:00:22.57 bitrate=   6.0kbits/sframe=   24 fps=1.1 q=0.0 size=      18kB time=00:00:23.56 bitrate=   6.2kbits/sframe=   24 fps=1.0 q=0.0 size=      18kB time=00:00:23.56 bitrate=   6.2kbits/sframe=   25 fps=1.1 q=0.0 size=      19kB time=00:00:24.56 bitrate=   6.4kbits/sframe=   25 fps=1.0 q=0.0 size=      19kB time=00:00:24.56 bitrate=   6.4kbits/sframe=   26 fps=1.1 q=0.0 size=      20kB time=00:00:25.55 bitrate=   6.5kbits/sframe=   26 fps=1.0 q=0.0 size=      20kB time=00:00:25.55 bitrate=   6.5kbits/sframe=   27 fps=1.0 q=0.0 size=      22kB time=00:00:26.54 bitrate=   6.7kbits/sframe=   27 fps=1.0 q=0.0 size=      22kB time=00:00:26.54 bitrate=   6.7kbits/sframe=   28 fps=1.0 q=0.0 size=      23kB time=00:00:27.58 bitrate=   6.8kbits/sframe=   28 fps=1.0 q=0.0 size=      23kB time=00:00:27.58 bitrate=   6.8kbits/sframe=   29 fps=1.0 q=0.0 size=      24kB time=00:00:28.58 bitrate=   6.9kbits/sframe=   30 fps=1.1 q=0.0 size=      25kB time=00:00:29.00 bitrate=   7.0kbits/sframe=   30 fps=1.0 q=0.0 size=      25kB time=00:00:29.57 bitrate=   7.0kbits/sframe=   30 fps=1.0 q=0.0 size=      25kB time=00:00:29.57 bitrate=   7.0kbits/sframe=   31 fps=1.0 q=0.0 size=      27kB time=00:00:30.56 bitrate=   7.2kbits/sframe=   32 fps=1.1 q=0.0 size=      27kB time=00:00:30.56 bitrate=   7.2kbits/sframe=   32 fps=1.0 q=0.0 size=      28kB time=00:00:31.56 bitrate=   7.3kbits/sframe=   33 fps=1.1 q=0.0 size=      29kB time=00:00:32.55 bitrate=   7.4kbits/sframe=   33 fps=1.0 q=0.0 size=      29kB time=00:00:32.55 bitrate=   7.4kbits/sframe=   33 fps=1.0 q=0.0 size=      29kB time=00:00:32.55 bitrate=   7.4kbits/sframe=   34 fps=1.0 q=0.0 size=      31kB time=00:00:33.54 bitrate=   7.4kbits/sframe=   35 fps=1.1 q=0.0 size=      31kB time=00:00:33.96 bitrate=   7.5kbits/sframe=   35 fps=1.0 q=0.0 size=      32kB time=00:00:34.53 bitrate=   7.5kbits/sframe=   36 fps=1.0 q=0.0 size=      33kB time=00:00:35.58 bitrate=   7.6kbits/sframe=   36 fps=1.0 q=0.0 size=      33kB time=00:00:35.58 bitrate=   7.6kbits/sframe=   36 fps=1.0 q=0.0 size=      33kB time=00:00:35.58 bitrate=   7.6kbits/sframe=   37 fps=1.0 q=0.0 size=      34kB time=00:00:36.57 bitrate=   7.7kbits/sframe=   38 fps=1.0 q=0.0 size=      36kB time=00:00:37.56 bitrate=   7.8kbits/sframe=   38 fps=1.0 q=0.0 size=      36kB time=00:00:37.56 bitrate=   7.8kbits/sframe=   39 fps=1.0 q=0.0 size=      37kB time=00:00:38.56 bitrate=   7.8kbits/sframe=   39 fps=1.0 q=0.0 size=      37kB time=00:00:38.56 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q=0.0 size=      46kB time=00:00:45.56 bitrate=   8.2kbits/sframe=   46 fps=1.0 q=0.0 size=      46kB time=00:00:45.56 bitrate=   8.2kbits/sframe=   47 fps=1.0 q=0.0 size=      47kB time=00:00:46.55 bitrate=   8.3kbits/sframe=   47 fps=1.0 q=0.0 size=      47kB time=00:00:46.55 bitrate=   8.3kbits/sframe=   48 fps=1.0 q=0.0 size=      48kB time=00:00:47.54 bitrate=   8.3kbits/sframe=   48 fps=1.0 q=0.0 size=      48kB time=00:00:47.54 bitrate=   8.3kbits/sframe=   49 fps=1.0 q=0.0 size=      50kB time=00:00:48.59 bitrate=   8.4kbits/sframe=   49 fps=1.0 q=0.0 size=      50kB time=00:00:48.59 bitrate=   8.4kbits/s[flv @ 0000009a40865940] Packets poorly interleaved, failed to avoid negative timestamp -3900 in stream 0.
Try -max_interleave_delta 0 as a possible workaround.
[flv @ 0000009a40865940] Packets are not in the proper order with respect to DTS
av_interleaved_write_frame(): Invalid argument
[flv @ 0000009a40865940] Failed to update header with correct duration.
[flv @ 0000009a40865940] Failed to update header with correct filesize.
frame=   50 fps=1.0 q=6.0 Lsize=      63kB time=00:00:49.11 bitrate=  10.5kbits/s speed=   1x
video:27kB audio:48kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: unknown
[libx264 @ 0000009a4082ddc0] frame I:1     Avg QP: 0.56  size: 27197
[libx264 @ 0000009a4082ddc0] frame P:15    Avg QP: 0.76  size:  2567
[libx264 @ 0000009a4082ddc0] frame B:34    Avg QP: 3.98  size:  1481
[libx264 @ 0000009a4082ddc0] consecutive B-frames:  8.0%  0.0% 12.0% 80.0%
[libx264 @ 0000009a4082ddc0] mb I  I16..4: 13.1% 13.8% 73.1%
[libx264 @ 0000009a4082ddc0] mb P  I16..4:  0.0%  0.1%  0.8%  P16..4: 17.5%  5.9%  4.2%  0.0%  0.0%    skip:71.5%
[libx264 @ 0000009a4082ddc0] mb B  I16..4:  0.0%  0.0%  0.3%  B16..8: 12.1%  4.2%  2.4%  direct: 6.3%  skip:74.7%  L0:42.9% L1:41.8% BI:15.4%
[libx264 @ 0000009a4082ddc0] final ratefactor: -7.50
[libx264 @ 0000009a4082ddc0] 8x8 transform intra:12.3% inter:14.5%
[libx264 @ 0000009a4082ddc0] coded y,uvDC,uvAC intra: 95.2% 96.9% 96.9% inter: 16.0% 14.9% 14.8%
[libx264 @ 0000009a4082ddc0] i16 v,h,dc,p: 26% 32% 32% 11%
[libx264 @ 0000009a4082ddc0] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu:  8% 40% 14%  8%  1%  2%  1%  1% 25%
[libx264 @ 0000009a4082ddc0] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 15% 45%  7%  4%  5%  3%  7%  3%  9%
[libx264 @ 0000009a4082ddc0] i8c dc,h,v,p: 36% 40% 18%  6%
[libx264 @ 0000009a4082ddc0] Weighted P-Frames: Y:0.0% UV:0.0%
[libx264 @ 0000009a4082ddc0] ref P L0: 65.2%  2.2% 19.9% 12.7%
[libx264 @ 0000009a4082ddc0] ref B L0: 71.8% 23.0%  5.2%
[libx264 @ 0000009a4082ddc0] ref B L1: 88.2% 11.8%
[libx264 @ 0000009a4082ddc0] kb/s:17.86
Conversion failed!


  


  Main message from above was :

  


  [flv @ 0000009a40865940] Packets poorly interleaved, failed to avoid negative timestamp -3900 in stream 0.


  


  It was problem to stream 0 so it was mixed sounds stream BUT earlier it was fine with mixing

  


  and sending mix over internet BUT after I added screen view and scaling it failed to work.

  


  What is problem ?

  


  How to fix it ?

  


  Since I was able to do this to stream to disc I would assume that

  


  computer processing power is enough. Since I was able to stream over internet mixed sounds I

  


  would assume that it is not problem here. So the problem must be with sending

  


  screen view. BUT I put framerate 1 per second and downsized its resolution. I compressed

  


  sounds as much as I could. I added -b:a and -b:v commands to reduce network flow.

  


  WHAT ELSE COULD I DO TO FIX IT ?

  


• What Are Website KPIs (10 KPIs and Best Ways to Track Them)

  3 mai 2024, par Erin

  Trying to improve your website’s performance ?

  Have you ever heard the phrase, “What gets measured gets managed ?”

  To improve, you need to start crunching your numbers.

  The question is, what numbers are you supposed to track ?

  If you want to improve your conversions, then you need to track your website KPIs.

  In this guide, we’ll break down the top website KPIs you need to be tracking and how you can track them so you can double down on what’s working with your website (and ditch what’s not).

  Let’s begin.

  What are website KPIs ?

  Before we dive into website KPIs, let’s define “KPI.”

  A KPI is a key performance indicator.

  You can use this measurable metric to track progress toward a specific objective.

  A website KPI is a metric to track progress towards a specific website performance objective.

  

  Website KPIs help your business identify strengths and weaknesses on your website, activities you’re doing well (and those you’re struggling with).

  Web KPIs can give you and your team a target to reach with simple checkpoints to show you whether you’re on the right track toward your goals.

  By tracking website KPIs regularly, you can ensure your organisation performs consistently at a high level.

  Whether you’re looking to improve your traffic, leads or revenue, keeping a close eye on your website KPIs can help you reach your goals.

  10 Website KPIs to track

  If you want to improve your site’s performance, you need to track the right KPIs.

  While there are plenty of web analytics solutions on the market today, below we’ll cover KPIs that are automatically tracked in Matomo (and don’t require any configuration).

  Here are the top 10 website KPIs you need to track to improve site performance and grow your brand :

  1. Pageviews

  Website pageviews are one of the most important KPIs to track.

  What is it exactly ?

  It’s simply the number of times a specific web page has been viewed on your site in a specific time period.

  For example, your homepage might have had 327 pageviews last month, and only 252 this month. 

  This is a drop of 23%. 

  A drop in pageviews could mean your search engine optimisation or traffic campaigns are weakening. Alternatively, if you see pageviews rise, it could mean your marketing initiatives are performing well.

  High or low pageviews could also indicate potential issues on specific pages. For example, your visitors might have trouble finding specific pages if you have poor website structure.

  

  2. Average time on page

  Now that you understand pageviews, let’s talk about average time on page.

  This is simple : it’s the average amount of time your visitors spend on a particular web page on your site.

  This isn’t the average time they spend on your website but on a specific page.

  If you’re finding that you’re getting steady traffic to a specific web page, but the average time on the page is low, it may mean the content on the page needs to be updated or optimised.

  Tracking your average time on page is important, as the longer someone stays on a page, the better the experience.

  This isn’t a hard and fast rule, though. For specific types of content like knowledge base articles, you may want a shorter period of time on page to ensure someone gets their answer quickly.

  3. Bounce rate

  Bounce rate sounds fun, right ?

  Well, it’s not usually a good thing for your website.

  A bounce rate is how many users entered your website but “bounced” away without clicking through to another page.

  Your bounce rate is a key KPI that helps you determine the quality of your content and the user experience on individual pages.

  You could be getting plenty of traffic to your site, but if the majority are bouncing out before heading to new pages, it could mean that your content isn’t engaging enough for your visitors.

  Remember, like average time on page, your bounce rate isn’t a black-and-white KPI.

  A higher bounce rate may mean your site visitors got exactly what they needed and are pleased.

  But, if you have a high bounce rate on a product page or a landing page, that is a sign you need to optimise the page.

  4. Exit rate

  Bounce rate is the percentage of people who left the website after visiting one page.

  Exit rate, on the other hand, is the percentage of website visits that ended on a specific page.

  For example, you may find that a blog post you wrote has a 19% exit rate and received 1,000 visits that month. This means out of the 1,000 people who viewed this page, 190 exited after visiting it.

  On the other hand, you may find that a second blog post has 1,000 pageviews, but a 10% exit rate, with only 100 people leaving the site after visiting this page.

  What could this mean ?

  This means the second page did a better job keeping the person on your website longer. This could be because :

  • It had more engaging content, keeping the visitors’ interest high
  • It had better internal links to other relevant pieces of content
  • It had a better call to action, taking someone to another web page

  If you’re an e-commerce store and notice that your exit rate is higher on your product, cart or checkout pages, you may need to adjust those pages for better conversions.

  

  5. Average page load time

  Want to know another reason you may have a high exit rate or bounce rate on a page ?

  Your page load time.

  The average page load time is the average time it takes (in seconds) from the moment you click through to a page until it has fully rendered within your browser.

  In other words, it’s the time it takes after you click on a page for it to be fully functional.

  Your average load time is a crucial website KPI because it significantly impacts page performance and the user experience.

  How important is your page load time ?

  Nearly 53% of website visitors expect e-commerce pages to load in 3 seconds or less.

  You will likely lose visitors if your pages take too long to load.

  You could have the best content on a web page, but if it takes too long to load, your visitors will bounce, exit, or simply be frustrated.

  6. Conversions

  

  Conversions.

  It’s one of the most popular words in digital marketing circles.

  But what does it mean ?

  A conversion is simply the number of times someone takes a specific action on your website.

  For example, it could be wanting someone to :

  • Read a blog post
  • Click an external link
  • Download a PDF guide
  • Sign up to your email list
  • Comment on your blog post
  • Watch a new video you uploaded
  • Purchase a limited-edition product
  • Sign up for a free trial of your software

  To start tracking conversions, you need to first decide what your business goals are for your website.

  With Matomo, you can set up conversions easily through the Goals feature. Simply set up your website goals, and Matomo will automatically track the conversions towards that objective (as a goal completion).

  Simply choose what conversion you want to track, and you can analyse when conversions occur through the Matomo platform.

  7. Conversion rate

  

  Now that you know what a conversion is, it’s time to talk about conversion rate.

  This key website KPI will help you analyse your performance towards your goals.

  Conversion rate is simply the percentage of visitors who take a desired action, like completing a purchase, signing up for a newsletter, or filling out a form, out of the total number of visitors to your website or landing page.

  Understanding this percentage can help you plan your marketing strategy to improve your website and business performance.

  For instance, let’s say that 2% of your website visitors purchase a product on your digital storefront.

  Knowing this, you could tweak different levers to increase your sales.

  If your average order value is $50 and you get 100,000 visits monthly, you make about $100,000.

  Let’s say you want to increase your revenue.

  One option is to increase your traffic by implementing campaigns to increase different traffic sources, such as social media ads, search ads, organic social traffic, and SEO.

  If you can get your traffic to 120,000 visitors monthly, you can increase your revenue to $120,000 — an additional $20,000 monthly for the extra 20,000 visits.

  Or, if you wanted to increase revenue, you could ignore traffic growth and simply improve your website with conversion rate optimisation (CRO).

  CRO is the practice of making changes to your website or landing page to encourage more visitors to take the desired action.

  If you can get your conversion rate up to 2.5%, the calculation looks like this :

  100,000 visits x $50 average order value x 2.5% = $125,000/month.

  8. Average time spent on forms

  If you want more conversions, you need to analyse forms.

  Why ?

  Form analysis is crucial because it helps you pinpoint where users might be facing obstacles. 

  By identifying these pain points, you can refine the form’s layout and fields to enhance the user experience, leading to higher conversion rates.

  In particular, you should track the average time spent on your forms to understand which ones might be causing frustration or confusion. 

  The average time a visitor spends on a form is calculated by measuring the duration between their first interaction with a form field (such as when they focus on it) and their final interaction.

  Find out how Concrete CMS tripled their leads using Form Analytics.

  9. Play rate

  One often overlooked website KPI you need to be tracking is play rate.

  What is it exactly ?

  The percentage of visitors who click “play” on a video or audio media format on a specific web page.

  For example, if you have a video on your homepage, and 50 people watched it out of the 1,000 people who visited your website today, you have a play rate of 5%.

  Play rate lets you track whenever someone consumes a particular piece of audio or video content on your website, like a video, podcast, or audiobook.

  Not all web analytics solutions offer media analytics. However, Matomo lets you track your media like audio and video without the need for configuration, saving you time and upkeep.

  10. Actions per visit

  Another crucial website KPI is actions per visit.

  This is the average number of interactions a visitor has with your website during a single visit.

  For example, someone may visit your website, resulting in a variety of actions :

  • Downloading content
  • Clicking external links
  • Visiting a number of pages
  • Conducting specific site searches

  Actions per visit is a core KPI that indicates how engaging your website and content are.

  The higher the actions per visit, the more engaged your visitors typically are, which can help them stay longer and eventually convert to paying customers.

  Track your website KPIs with Matomo today

  Running a website is no easy task.

  There are dozens of factors to consider and manage :

  • Copy
  • Design
  • Performance
  • Tech integrations
  • And more

  But, to improve your website and grow your business, you must also dive into your web analytics by tracking key website KPIs.

  Managing these metrics can be challenging, but Matomo simplifies the process by consolidating all your core KPIs into one easy-to-use platform.

  As a privacy-friendly and GDPR-compliant web analytics solution, Matomo tracks 20-40% more data than other solutions. So you gain access to 100% accurate, unsampled insights, enabling confident decision-making.

  Join over 1 million websites that trust Matomo as their web analytics solution. Try it free for 21 days — no credit card required.

  Try Matomo for Free

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• What is last click attribution ? A beginner’s guide

  10 mars 2024, par Erin

  Imagine you just finished a successful marketing campaign. You reached new highs in campaign revenue. Your conversion was higher than ever. And you did it without dramatically increasing your marketing budget.

  So, you start planning your next campaign with a bigger budget.

  But what do you do ? Where do you invest the extra money ?

  You used several marketing tactics and channels in the last campaign. To solve this problem, you need to track marketing attribution — where you give conversion credit to a channel (or channels) that acted as a touchpoint along the buyer’s journey.

  One of the most popular attribution models is last click attribution.

  In this article, we’ll break down what last click attribution is, its advantages and disadvantages, and examples of how you can use it to gain insights into the marketing strategies driving your growth.

  What is last click attribution ?

  Last click, or last interaction, is a marketing attribution model that seeks to give all credit for a conversion to the final touchpoint in the buyer’s journey. It assumes the customer’s last interaction with your brand (before the sale) was the most influential marketing channel for the conversion decision.

  

  Example of last click attribution

  Let’s say a woman named Jill stumbles across a fitness equipment website through an Instagram ad. She explores the website, looking at a few fitness bands and equipment, but she doesn’t buy anything.

  A few days later, Jill was doing a workout but wished she had equipment to use.

  So, she Googles the name of the company she checked out earlier to take a look at the fitness bands it offers. She’s not sure which one to get, but she signs up for a 10% discount by entering her email.

  A few days later, she sees an ad on Facebook and visits the site but exits before purchasing. 

  The next day, Jill gets an email from the store stating that her discount code is expiring. She clicks on the link, plugs in the discount code, and buys a fitness band for $49.99.

  Under the last click attribution model, the fitness company would attribute full credit for the sale to their email campaign while ignoring all other touchpoints (the Instagram ad, Jill’s organic Google search, and the Facebook ad).

  3 advantages of last click attribution

  Last click attribution is one of the most popular methods to credit a conversion. Here are the primary advantages of using it to measure your marketing efforts :

  

  1. Easiest attribution method for beginners

  If something’s too complicated, many people simply won’t touch it.

  So, when you start diving into attribution, you might want to keep it simple. Fortunately, last click attribution is a wonderful method for beginner marketers to try out. And when you first begin tracking your marketing efforts, it’s one of the easiest methods to grasp. 

  2. It can have more impact on revenue

  Attribution and conversions go hand in hand. But conversions aren’t just about making a sale or generating more revenue. We often need to track the conversions that take place before a sale.

  This could include gaining a new follower on Instagram or capturing an email subscriber with a new lead magnet.

  If you’re trying to attribute why someone converted into a follower or lead, you may want to ditch last click for something else.

  But when you’re looking strictly at revenue-generating conversions, last click can be one of the most impactful methods for giving credit to a conversion.

  3. It helps you understand bottom-of-funnel conversions

  If SEO is your focus, chances are pretty good that you aren’t looking for a direct sale right out of the gate. You likely want to build your authority, inform and educate your audience, and then maybe turn them into a lead.

  However, when your primary focus isn’t generating traffic or leads but turning your leads into customers, then you’re focused on the bottom of your sales funnel.

  Last click can be helpful to use in bottom-of-funnel (BoFu) conversions since it often means following a paid ad or sales email that allows you to convert your warm audience member.

  If you’re strictly after revenue, you may not need to pay as much attention to the person who reads your latest blog post. After they read the article, they may have seen a social media post. And then, maybe they saw your email with a discount to buy now — which converted them into a paying customer.

  3 challenges of last click attribution

  Last click attribution is a simple way to start analysing the channels that impact your conversions. But it’s not perfect.

  Here are a few challenges of last click attribution you should keep in mind :

  

  1. It ignores all other touchpoints

  Last click attribution is a single-touch attribution model. This type of model declares that a single channel gets 100% of the credit for a sale.

  But this can overlook impactful contributions from other channels.

  Multi-touch attribution seeks to give credit to multiple channels for each conversion. This is a more holistic approach.

  2. It fragments the customer journey

  Most customers need a few touchpoints before they’ll make a purchase.

  Maybe it’s reading a blog post via Google, checking out a social media post on Instagram, and receiving a nurture email.

  If you look only at the last touchpoint before a sale, then you ignore the impact of the other channels. This leads to a fragmented customer journey. 

  Imagine this : You tell your marketing leaders that Facebook ads are responsible for your success because they were the last touch for 65% of conversions. So, you pour your entire budget into Facebook ads.

  What happens ?

  Your sales drop by 60% in one month. This happens because you ignored the traffic you were generating from SEO blog posts that led to that conversion — the nurturing that took place in email marketing.

  3. Say goodbye to brand awareness marketing

  Without a brand, you can’t have a sustainable business.

  Some marketing activities, like brand awareness campaigns, are meant to fuel brand awareness to build a business that lasts for years.

  But if you’re going to use last click attribution to measure the effectiveness of your marketing efforts, then you’re going to diminish the impact of brand awareness.

  Your brand, as a whole, has the ability to generate multiples of your current revenue by simply reaching more people and creating unique brand experiences with new audiences.

  Last click attribution can’t easily measure brand awareness activities, which means their importance is often ignored.

  Last click attribution vs. other attribution models

  Last click attribution is just one type of attribution model. Here are five other common marketing attribution models you might want to consider :

  

  First interaction

  We’ve already touched on last click interaction as a marketing attribution model. But one of the most common models does the opposite.

  First interaction, or first touch, gives full credit to the first channel that brought a lead in. 

  First interaction is best used for top-of-funnel (ToFU) conversions, like user acquisition.

  Last non-direct interaction

  A similar model to last click attribution is one called last non-direct interaction. But one major difference is that it excludes all direct traffic from the calculation. Instead, it assigns full conversion credit to the channel that precedes it.

  For instance, let’s say you see someone comes to your website via a Facebook ad but doesn’t purchase. Then one week later, they go directly to your website through a bookmark they saved and they complete a purchase. Instead of giving attribution to the direct traffic touchpoint (entering your site through a saved bookmark), you attribute the conversion to the previous channel.

  In this case, the Facebook ad gets the credit.

  Last non-direct attribution is best used for BoFu conversions.

  Linear

  Another common attribution model is called linear attribution. Here, you split the credit for a conversion equally across every single touchpoint.

  This means if someone clicks on your blog post in Google, TikTok post, email, and a Facebook ad, then the credit for the conversion is equally split between each of these channels.

  This model is helpful for looking at both BoFu and ToFu activities.

  Time decay

  Time decay is an attribution model that more accurately credits conversions across different touchpoints. This means the closer a channel is to a conversion, the more weight is given to it.

  The time decay model assumes that the closer a channel is to a conversion, the greater that channel’s impact is on a sale.

  Position based

  Position-based, also called U-shaped attribution, is an interesting model that gives multiple channels credit for a conversion.

  But it doesn’t give equal credit to channels or weighted credit to the channels closest to the conversion.

  Instead, it gives the most credit to the first and last interactions.

  In other words, it emphasises the conversion of someone to a lead and, eventually, a customer.

  It gives the first and last interaction 40% of the credit for a conversion and then splits the remaining 20% across the other touchpoints in the customer journey.

  If you’re ever unsure about which attribution model to use, with Matomo, you can compare them to determine the one that best aligns with your goals and accurately reflects conversion paths. 

  

  In the above screenshot from Matomo, you can see how last-click compares to first-click and linear models to understand their respective impacts on conversions.

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  Use Matomo to track last click attribution

  If you want to improve your marketing, you need to start tracking your efforts. Without marketing attribution, you will never be certain which marketing activities are pushing your business forward.

  Last click attribution is one of the most popular ways to get started with attribution since it, very simply, gives full credit to the last interaction for a conversion.

  If you want to start tracking last click attribution (or any other previously mentioned attribution model), sign up for Matomo’s 21-day free trial today. No credit card required.

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