May 3, 2020

Larix Grove for distributing Larix Broadcaster settings

Usually all connections in Larix Broadcaster are set up in application settings, and you need to add each connection manually.

With Larix Grove you can distribute and import streaming settings using a special URL format.
You make a URL with Grove wizard and send it via email, messenger, a web page or QR code.

When you tap on the link on a web page, Larix Broadcaster is launched and you need to confirm settings import.

You can create a QR code and once you scan it, you will import connections settings.

You may also open the Settings -> Connections -> Import Larix setting app menu to process the links manually copied from an email or a messenger.

Try Larix Grove in action and let us know of any ideas to improve it.

Here's a demo video showing this feature in action.


April 22, 2020

Handling fuzzy FPS to get proper bitrate output

Nimble Live Transcoder allows creating transcoding scenarios with various transformations, including control over output bitrate. Usually you have some input stream with expected parameters like frame rate (FPS) and resolution and want to create one or more output with certain bitrate.

Normally you can define output bitrate using various parameters of encoder libraries, like it's described for x264 bitrate, constant x264 bitrate, NVENC bitrate and QuickSync bitrate setup in respective articles.

However, some sources may produce streams with uncertain frame rate. For example, mobile encoders may produce variable bitrate so when your users are streaming with apps like Larix Broadcaster you may get streams with unexpected FPS. As a result, if you ingest such fuzzy stream into a transcoding scenario, the encoder may produce the unexpected bitrate - higher or lower than you've defined in encoder settings.

You can use two approaches to handle this: by adding FPS filter or by defining FPS numerator and denominator parameters for encoder.

Option A: Add FPS filter


The best way to handle this is to set some definite value for FPS using a custom "fps" filter.
If you expect your users to stream with framerate around 25-30 FPS, you can set filter to 30 FPS. If you need a better framerate, just set it to proper value like 60 FPS. This filter uses optimized algorithm to insert extra duplicate frames, and encoders are optimized to process such repeating frames, so this will not add any processing overhead.

A.1 Add video_filter_reset_by_pts_enabled parameter to config

Before setting a filter, add video_filter_reset_by_pts_enabled = true parameter into your nimble.conf. With FPS filter, if you have MPEG-TS input (e.g. if you use SRT) and if input gets time gap, the output may hang up, so this parameter avoids that.

A.2 Add filter

Here's how you add the filter into transcoding scenario. Open your scenario and drag a Custom filter onto a pipeline. 


Then connect it to other filters and the encoder.


Now if you have your bitrate set up in the encoder element, you'll get the output bitrate which you've defined there after you save the scenario.

Option B: Using FPS numerator and denominator


Another option is to set output FPS numerator and denominator. All Transcoder encoders' libraries support them, so just set them as part of parameters: fps_n for numerator and fps_d for denominator. These parameters will "introduce" the encoder to the numbers which it will use for further bitrate calculation.

If you need 30 FPS, use fps_n=30 and fps_d=1, as shown below. The same formula applies to 60 FPS: use fps_n=60 and fps_d=1.


In this example, once you save encoder setting and then save scenario, the encoder will be likely to produce 1Mbps output based on expected frame rate of 30 FPS.



Let us know if you have any questions about these approaches.

Related documentation


Nimble Live Transcoder, Live Transcoder documentation reference,

April 20, 2020

RIST protocol in Nimble Streamer

RIST (Reliable Internet Streaming Transport) protocol is a new protocol for low-latency live video over un-managed networks. You can find out more about this protocol in RIST website. RIST is developed and promoted by RIST Forum where Softvelum is an affiliate member.

RIST is available via libRIST open-source library so Softvelum uses it to integrate this technology into Nimble Streamer media server. We give our customers all delivery options available on the market so libRIST is now one more delivery protocol in our stack.


Nimble Streamer allows both receiving (Listen and Pull) and sending (Listen and Push) RIST streams.

All supported protocols can be used as input for re-packaging into RIST: SRT, RTMP, RTSP, MPEGTS, HLS and Icecast.

Having any RIST input stream, it can be re-packaged into all supported output protocols: HLS, MPEG-DASH, SRT, RTMP, RTSP, MPEGTS and Icecast, as well as recorded into DVR.

This article describes the steps to set up RIST streaming via Nimble Streamer.

Install Nimble Streamer and RIST package


RIST is available via separate package for Nimble Streamer. You need to install it in order to use this protocol.
  1. Sign up for WMSPanel account if you haven't done it yet.
  2. Install Nimble Streamer or upgrade it to latest version.
  3. Follow instruction RIST page to install RIST package. RIST package is currently available only on Ubuntu 18.04 and later versions.
Now you may proceed with the setup.

Receiving RIST streams


Being logged into WMSPanel, click on Nimble Streamer -> Live streams setting top menu. Then choose MPEGTS In tab.


Click on Add RIST stream button to open the dialog shown below.

RIST provides two modes for obtaining the stream:

  • Listen sets Nimble to wait for incoming data and process it as soon as it arrives. Your source needs to be set "Push" mode.
  • Pull sets Nimble to initiate the source server to start sending the data. Your source needs to be set to Listen mode.
If you select Listen, you'll need to specify the local interface - Local IP and Local port - which Nimble will listen to in order to get a stream. If you'd like it to listen to all interfaces, just set Local IP to 0.0.0.0.



Alias is the name that will be used in incoming streams list.

Also check Add outgoing stream checkbox and fill in the Application name and Stream name fields if you'd like to automatically create outgoing stream for further processing. This step is specific to MPEG-TS streams in Nimble.

If you'd like get stream in Pull mode, you'll need to specify the Remote IP and Remote port where you'll get your stream from.


Once you save setting, the settings will be applied  create outgoing stream, you can use a breadcrumbs on top of settings page to go to live streams of specific server (by clicking "RIST demo" server link in this example):


You can now use this stream for further processing and playback similar to other MPEG-TS processed streams.

Sending RIST streams


We assume that you have a live stream ready to be sent out. If you don't, please refer to live streaming scenarios to see how you can set it up.

Go to UDP streaming tab to add new setting.


Click on Add RIST setting to see dialog below.
RIST allows streaming in two modes:

  • Push will initiate the active sending of the selected channel to the destination IP/Port which is working in Listen mode.
  • Listen will make Nimble Streamer wait for the Pull command from remote server.

Mode is a field to set this behavior.


If you choose Push mode, you'll see Remote IP and Remote port fields to specify the destination.

If you choose Listen, you'll be able to specify Local IP and Local port to be used for listening for a connection. Just like in receiving use case, If you'd like it to listen to all interfaces, just set Local IP to 0.0.0.0.


Source application name, Source stream name and their respective PMT PIDs, Video PIDs and Audio PIDs describe where the content is taken from for further transmission. Those streams need to be defined prior to making this setup. You may define multiple sources - in this case RIST channel will have multiple streams in it.

Supported RIST parameters


Nimble Streamer allows using the following parameters. You need to set them both on sender and receiver side. You can refer to RIST documentation for more details and tips on their usage.

  • aes-type is encryption type, set if only secret is not empty. Available values are are: 0 - none, 128 - AES-128, 192 - AES-192, 256 - AES-256.
  • bandwidth is RIST recovery bandwidth (Kbit/s), it's 100000 by default
  • buffer is maximum RIST recovery buffer size in milliseconds, it's 1000 by default.
  • cname is a manually configured identifier.
  • compression defines compression, 0 is for disabled, 1 is for enabled.
  • keepalive-interval is a Keep-alive timeout in milliseconds, it's "100" by default.
  • miface is multicast interface name.
  • profile is RIST profile. Available values are are: simple, main (default)
  • reorder-buffer is reorder buffer size in milliseconds, it's 25 by default.
  • return-bandwidth is RIST recovery return bandwidth (Kbit/s), it's 0 by default
  • rist-logging is logging level. Available values are are: quiet (default), info, error, warn, debug, simulate
  • rtt is round-trip-time (RTT) in milliseconds, it's 500 by default.
  • secret is encryption password.
  • session-timeout is a session timeout, in milliseconds.
If you have any questions about these parameters, please refer to librist documentation as describe below.

libRIST


Nimble Streamer uses librist library version 2.6.5. If you have any questions on RIST parameters usage and other topics, please refer to RIST documentation. and contact RIST team about it.


Related documentation


Softvelum Nimble Streamer, MPEG-TS support in Nimble Streamer

April 12, 2020

Nimble Streamer DRM with Widevine, FairPlay, Playready, EZDRM and VCAS

Softvelum team is glad to announce that Nimble Streamer now has full support for a number of Digital Rights Management (DRM) capabilities for live streaming.

The following encryption engines are supported to protect MPEG-DASH:
  • Google Widevine™
  • Microsoft Playready™
Also, Apple FairPlay is supported t protect Apple HLS streams.

The following key management servers are supported:
  • Widevine Cloud Service with key rotation
  • EZDRM™ key management support for Widevine, FairPlay and Playready
Also, previously supported Verimatrix™ VCAS key management for HLS protection is fully supported.

DRM configuration is performed via easy-to-use configuration file which allows defining per-application setting for all available DRM features.

Visit DRM feature page to learn more about DRM setting.

Nimble DRM is part of Nimble Addenda premium package which requires a license accessible via monthly subscription. Addenda covers Nimble DRM and Nimble Advertizer capabilities.

Please read Nimble DRM spec and subscribe for a license to try it in action.

Later on we'll release some other popular key management systems, let us know which one you're looking for the most.

Related documentation


Nimble Streamer DRM, Nimble Addenda, Nimble Advertizer

April 7, 2020

iCloud support and file operations in Larix Broadcaster for iOS

Larix Broadcaster for iOS now has improved capabilities for recording live stream into local files.

Here are the options you can use for files storage:

  • Local storage available via MacOS Finder as described in this article.
  • iCloud Drive
  • Photo Library

You can also split video into sections, like it's usually done in dash cams. The recording is divided into multiple files by length.

Here's how you can set this up.

First, install Larix Broadcaster from AppStore.

Go to app Settings / Capture and recording / Record menu.


First, turn on the Record stream to automatically record any live stream.

Tap on Storage to select the default storage for recorded videos and screenshots. This will be one of 3 options mentioned above.

Split video into sections allows defining the length of recording which the stream will be split into. By default of Off.

Once you make recordings, go to Settings / Manage saved files menu.


Here you can long tap on file name to move it to proper destination. You can also tap Edit to perform multiple files operation. The iCloud tab will show the content of respective iCloud Drive folder. The recorded or copied files an be found in respective folders.

Take a look at a brief video overview of this feature.





Let us know if you'd like any improve in this feature set.

Related documentation


Larix Broadcaster, Larix documentation reference, Softvelum YouTube channel,

April 2, 2020

SRT FEC (forward error correction) support in Nimble Streamer

Softvelum is an active adopter of SRT technology and Nimble Streamer has extended support for it.

One of the features introduced in latest SRT library versions is the ability to set custom packet filters for SRT transmission. The first introduced built-in filter is Forward Error Correction (FEC).

Before using feature, please read carefully the SRT Packet Filtering & FEC feature documentation in SRT library github repo.

1. Disclaimer


We assume you are already familiar with SRT setup and usage, and you've successfully used SRT in other scenarios and use cases.

Before proceeding further, set up a test streaming scenario and make sure it works without any filters.

FEC filter is still under development and we've added it per requests from our customers.
Here is what you need to consider before using it:
  • Use FEC filter feature on your own risk.
  • It may crash the server, so if you face any issues, check Nimble Streamer logs to analyse the problem.
  • Read Known issues section below in case of issues.
  • Try using it with test servers and test streams first, and then move to production only when you make sure it works as expected.

2. Upgrade


In order to use this filter, you must upgrade Nimble Streamer and make sure you have the latest SRT library package.

  1. Nimble Streamer version must be at least 3.6.5-6, use this procedure to upgrade.
  2. SRT library package must be at least 1.4.1, use this page to get upgrade instruction.

Once you upgrade and re-start Nimble Streamer instance, you may proceed to further setup.

3. Setup details


According to information from SRT developers team, the FEC filter must be set on both sender and receiver sides and at least one side should define a configuration for it. In our example we'll define configuration parameter on sender.

As was mentioned, we assume you've set up your SRT streaming scenario. Let's modify it to set up sender part.

3.1 Sender


Go to "UDP streaming" tab on "Live streams setting" page and open your SRT setting. Scroll down to parameters list and add new "filter" parameter with a value which you find appropriate, as shown on a screenshot below.



We use "fec,cols:10,rows:5" there just as example, but you can use any other value which you find appropriate for your case, please refer to FEC documentation to learn more.

As you see we use latency and maxbw parameters as described in this article, we highly recommend always using them for your cases as well.

3.2 Receiver


Now on a receiver side, you need to define "filter" parameter with "fec" value. Notice that you don't need to set more details because you've defined them earlier on sender side.

In case of Nimble Streamer setup, go to "Live streams settings" page, "MPEGTS In" tab and add incoming stream. Then enter "filter" parameter with "fec" value as shown below.



This was an example of FEC usage in Nimble Streamer.

4. Known issues


In case of any issues please analyse Nimble Streamer logs to get more details for further analysis.

As this feature is under development, it has a number of issues. We've faced one of the issues during testing.

When FEC is enabled and lots of packets are dropped, it does not recover:
If a large drop is simulated on the line, SRT gets it's self into a state where it's no longer transmitting packets:
The following message is displayed:
16:40:22.279871!W:SRT.c: FEC: LARGE DROP detected! Resetting all groups. Base: %1583134490 -> %1583139490(shift by 5000).
It's still not fixed as of April 3rd, 2020:

So if you find this error in your Nimble Streamer logs, just disable FEC filter for now.

This is also the advise for any other issues related to FEC: if you face any uncertainly, just remove FEC filter and use SRT without it because we won't be able to help you.

You can look for existing issues and solutions in SRT issues on github. You can post your questions there in case of concerns with FEC filter.

Related documentation


SRT support in Softvelum products, SRT in Nimble Streamer,

March 30, 2020

2020 Q1 summary

This first quarter of 2020 brought a lot of disruption into lives of billions of people. The unprecedented global measures to reduce the harm from pandemic require a lot of businesses to move online, work remotely and use live streaming more intensively.

Softvelum is fully committed to provide the best support to our customers, as always. Since the early days of inception, we were working remotely full-time. We were building and adjusting our business processes to keep the efficiency while expanding our team. Now when we have to self-isolate in order to stay healthy, we keep doing what we used to do through all these years, keeping the level of support on the same highest level. Feel free to contact our helpdesk and take a look at the list of our social networks at the bottom of this message to stay in touch with us.

With the extreme rise of interest for live streaming, we keep working on new features, here are updates from this quarter which we'd like to share with you.



Mobile products

Mobile streaming in on the rise now, so we keep improving it.




SRT

SRT protocol is being deployed into more products across the industry. Our company was among the first to implemented it into our products, and now we see more people building their delivery networks based on this technology. So we've documented this approach:
  • Glass-to-Glass Delivery with SRT: The Softvelum Way - a post for SRT Alliance blog about building delivery from mobile deice through Nimble media server into mobile player.
  • Glass-to-glass SRT delivery setup - a post in our blog describing setup full details.
  • All of our products - Nimble Streamer, Larix Broadcaster and SLDP Player - now use the latest SRT library version 1.4.1.
  • Just in case you missed, watch vMix video tutorial for streaming from Larix Broadcaster to vMix via SRT, which can also be used a the source for such delivery chain.


Live Transcoder

We are continuously improving Live Transcoder so this quarter we made a number of updates to make it more robust and efficient. Here are the latest features we've made.

  • You can now create transcoding pipelines based only on NVENC hardware acceleration which works for Ubuntu 18.04+. Read this setup article for more details.
  • FFmpeg custom builds are now supported. This allows using additional libraries that are not supported by Transcoder at the moment. Read this article for setup details.
  • Transcoder control API is now available as part of WMSPanel API. It's a good way to automate some basic control operations.


Nimble Streamer

Read SVG News article about how Riot Games build their streaming infrastructure with various products, including Nimble Streamer.

A number of updates are available for Nimble Streamer this quarter:

Also, take a look at the State of Streaming Protocols for 2020Q1.


If you'd like to get our future news and updates, please consider following our social networks. We've launched a Telegram channel recently and we now make more videos for our YouTube channel. As always, our TwitterFacebook and LinkedIn feeds keep showing our news.



Stay healthy and safe, our team will help you carry on!

The State of Streaming Protocols - 2020 Q1

Softvelum team keeps tracking the state of streaming protocols. It's based on stats from WMSPanel reporting service which handles data from Wowza Streaming Engine and Nimble Streamer servers. This quarter WMSPanel collected data about more than 17.8 billion views. Total view time for our server products is 3.04 billion hours this quarter, or 33+ million view hours per day.

The State of Streaming Protocols - Q4 2019
The State of Streaming Protocols - Q1 2020

You can compare these numbers with metrics from Q4 2019:

The State of Streaming Protocols - Q4 2019

You can see a slight decrease of HLS while shifting views to progressive download and MPEG-DASH

We'll keep tracking protocols to see the dynamics. Check our updates at FacebookTwitter, Telegram and LinkedIn.

If you'd like to use these stats, please refer to this article by original name and URL.

March 22, 2020

Building NVENC-only pipeline with Nimble Transcoder

Live Transcoder for Nimble Streamer provides wide feature set for transforming live content using both software libraries and hardware acceleration.

NVidia NVENC always fully supported in Live Transcoder for decoding and encoding but all filtering operations were performed using CPU. That caused extra resources usage to transfer processed frames among CPU, GPU and RAM.

Nimble Live Transcoder now allows building transcoding pipelines which are performed completely with NVidia GPU hardware acceleration. This is done using specific FFmpeg libraries which we use in addition to our own code.

We'll show you how to set up this NVENC-powered processing chain.

1. Installation and initial setup


We assume you've already set up Nimble Streamer, it's been set up to get an incoming live stream and you've tested basic streaming. In our example we'll use a stream which application name is "input" and stream name is "source".

If you're not familiar with Live Transcoder, take a look at Transcoder documentation reference.

Notice that the described functionality is available on Ubuntu 18.04 only. We'll support other upcoming LTS Ubuntu releases as well.

The basic steps to make NVENC working are as follows:

  1. Install the latest NVidia drivers on your server.
  2. Create a transcoder license and subscribe for it.
  3. Install Live Transcoder add-on.
  4. Create some simple scenario with CPU transcoding (e.g. downscale your stream to 240p). This way you'll make sure the transcoder was set up properly.
If you already have Transcoder installed, please run these commands to upgrade the package:
sudo apt-get update
sudo apt-get install nimble-transcoder

Now create a new scenario to start a new pipeline setup.

2. Decoder setup


Once you create a new scenario, drag and drop a blue decoder element onto the dashboard. There you need to specify "NVENC-ffmpeg" in Decoder field.


Once the incoming stream is received, Nimble Transcoder will use proper NVDEC/CUVID FFmpeg decoder: h264_cuvid, hevc_cuvid or mpeg2_cuvid. Each decoder has its set of options in case you'd like to fine-tune them or if you want to use extended feature set.

The GPU core number from GPU field will be used in the pipeline which you create. So all further filters and encoders will recognize the source GPU core and will execute their transformations there.

One of those features for all decoders is the ability to resize the frame during decoding. This operation is highly optimized and you can use it to reduce further resource usage. This is available via "resize" parameter as shown on a picture below.


This feature is specifically helpful when you have FullHD stream input and you need to downscale it further. This resolution requires a lot of resources to handle so if you make initial downscale to HD or even lower resolution, then all further operations will consume less RAM and processing power on GPU.

Notice that all forwarding features (subtitles and SCTE-35 markers forwarding) mentioned at the bottom of the dialog will work regardless of decoding option which you choose.

Now let's set up filtering.

3. Filtering


Once the frame is decoded you can process it via a set of ffmpeg filters which are able to control NVENC behavior.
Nimble Transcoder supports a number of those, here are the most frequently used:

  • "split" - allows creating several identical outputs from input video. It's available as a filter element in a tool box of Transcoder UI.
  • "scale_npp" performs frame scaling. You add a custom filter to your scenario, set its name to "scale_npp" and its value to resolution, e.g. "854:480" or "640:360".
  • "fps" is a filter which sets the frames per second value. It's also defined via custom filter.
Notice that scale_npp can have only one output.

Notice that regular Scale filter from the UI toolbox will not work with GPU-decoded frames as well as other regular ffmpeg filters just because the processing is done internally in GPU.

However, you can take the frame out of GPU and process it separately using "hwdownload" and "hwupload_cuda" filters. To add them, add a custom filter, set its name as mentioned and leave the value field empty. Your steps will be as follows:

  1. Add "hwdownload" to get the frame from GPU.
  2. Add "format" custom filter with "nv12" value to set proper frame format.
  3. After that you can use regular FFmpeg filters.
  4. Then add "hwupload_cuda" filter to put it back into GPU processing pipe.

Notice that it will increase RAM/CPU usage so use it only if you need to do something you cannot do on GPU.

Let us know if you need information about other filters.

4. Encoder setup


Having the content transformed via filters, you can now encode it. Add encoder element to your scenario and select "FFmpeg" in "Encoder" field.

Then define "Codec" field as either h264_nvenc or hevc_nvenc - for H.264/AVC or H.265/HEVC codecs respectively.


You can use any parameters applicable for h264_hvenc or hevc_nvenc encoders.

For h264_hvenc most popular parameters would be these:
  • "b" defines bitrate in Mbps. Example: "4.5M" for 4.5 Mbps.
  • "profile" defines encoding profile, its possible values are "baseline", "main", "high", "high444p".
  • "preset" stands for encoding preset, its values are "default", "slow", "medium", "fast", "hp", "hq", "bd", "ll", "llhq", "llhp", "lossless", "losslesshp".


For more encoder settings, refer to FFmpeg documentation.

Just like you saw in decoder element, all forwarding features from listed under Expert setup at the bottom of the dialog will work properly.

5. Audio setup


When you have video pipeline set up, you need to define audio part. If you don't need any sound transformation, you can add a passthrough for it just like it's described in other setup examples.

6. Example


We've made a video showing the example of setup process, take a look at it:



Here's what we set up there:

  • A decoder has a downscale to 720p as described in section 2 above.
  • A split filter which has 3 equal outputs.
  • One output goes directly to the encoder. It takes the downscaled frame and simply encodes it into live/stream_720 output. The encoding parameters are similar to what you see in section 4.
  • Another output it processed via Scale_npp filter which scales it to 480p. That filter is described in section 3. Its output is encoded to live/stream_480 output stream.
  • One more output of split filter goes through "Scale_npp" (to scale to 360p) to "Fps" filter which sets its "fps" value to "25". Then it's encoded into live/stream_360 output.
  • Audio input is passed through for all 3 available output renditions.

This scenario uses only NVENC capabilities for video processing. The output streams are then transmuxed into the output streaming protocols which you select in global server settings or specific settings for "live" application.

Later on we'll introduce a video tutorial showing this scenario creation step by step.

If you have any questions, issues or questions, please feel free to contact us.

Related documentation


Live Transcoder for Nimble Streamer, Transcoder documentation reference,