Planet VideoLAN

Welcome on Planet VideoLAN. This page gathers the blogs and feeds of VideoLAN's developers and contributors. As such, it doesn't necessarly represent the opinion of all the developers, the VideoLAN project, ...

VLC for Android

October 10, 2018

PIC versus pic

Rémi Denis-Courmont

Have you ever wondered what was the difference between the -fpic and -fPIC compiler command line flags were?

October 10, 2018 07:48 PM

October 01, 2018

Introducing dav1d: a new AV1 decoder

Jean-Baptiste Kempf

Introducing dav1d

AV1 is a new video codec by the Alliance for Open Media, composed of most of the important Web companies (Google, Facebook, Netflix, Amazon, Microsoft, Mozilla...).

AV1 has the potential to be up to 20% better than the HEVC codec, but the patents license is totally free, while HEVC patents licenses are insanely high and very confusing.

The reference decoder for AV1 is great, but it's a research codebase, so it has a lot to improve.

Therefore, the VideoLAN, VLC and FFmpeg communities have started to work on a new decoder, sponsored by the Alliance of Open Media.

The goal of this new decoder is:

  • be small,
  • be as fast as possible,
  • be very cross-platform,
  • correctly threaded,
  • libre and (actually) Open Source.

Without further ado, the code:


dav1d is called dav1d, because Dav1d is an AV1 Decoder

(Yes, that is a recursive acronym, no need to tell us...)


You can see a talk during VDD 2018 about dav1d:

VDD2018 dav1d presentation.

Technical details

Some technical details about dav1d:

  • written in C99 (without VLAs),
  • has asm in NASM/GAS syntax (no intrinsics),
  • uses meson/ninja as buildsystem,
  • currently works on x86, x64, ARMv7, ARMv8,
  • runs on Windows, Linux, macOS, Android, iOS,
  • licensed under BSD.


Currently the source code of dav1d is 1/10th of lines of code compared to libaom and its weight is 1/3rd of the binary size of libaom.

It currently uses 1/4th of the memory usage of libaom and uses a very limited amount of stack.

Depending on the threads conditions (see the video talk linked above), dav1d is more or less faster than libaom 1.0.0, but slower than libaom HEAD.
dav1d having almost no assembly code yet, this is not surprising, and is actually a good starting point for the future.

Of course, those metrics will evolve once we add more assembly code, and when the project evolves a bit more.


Is it production-ready?

Not yet, but you can start testing it and check how the API works for you.

Can I help?

Yes! We need C, ASM developers, but also app integrators and testers to give us feedback.

I need to ship an AV1 decoder with my OS, my hardware, my app. Can I do that?

Yes. dav1d is licensed under BSD for this very reason.

Please talk to us, if you need to get adaptations for your use-case (hybrid decoders, or specific platforms, for example).

BSD is not copyleft, why?

We want AV1 to be as popular as possible. This requires fast decoders, running everywhere. Therefore, we want to help everyone, even non-open-source software.

See RMS opinion on this subject.

October 01, 2018 04:59 PM

July 18, 2018

VLC for iOS and UWP 3.1.0 release

Jean-Baptiste Kempf

VLC 3.1.0 release

After a few months since the release of VLC 3.0, today we release VLC 3.1.0 on 2 mobile OSes: iOS and Windows Store (UWP).

This release brings ChromeCast integration to iOS and UWP, like it was present on desktop and Android versions.

ChromeCast and hardware encoding

However, it supports ChromeCast in a more performant way, because we added hardware encoders to those 2 platforms.
Indeed, here, for local streaming, we care more about speed and battery saving than we care about bandwidth efficiency, si hardware encoding is a good fit.

On iOS, we're using the standard VideoToolbox hardware encoding to produce H.264 streams, muxed in MKV.

On UWP, we're using Quick Sync Video for intel CPUs (that covers almost all CPUs since 3rd core generation).

In fact, VLC has a QSV encoder since 2013, but it's very rarely used, because people usually prefer software encode (x264). Here, we fixed it and modified it to work inside the UWP sandbox.


You should really read Caro's blogpost here!

But in that version you have:

  • ChromeCast,
  • 360 video support, with sensors,
  • Numerous bugfixes on the playback core (inherited mostly from VLC 3.0.1-3.0.3)
  • Some decoding speed improvements,
  • Quite a few interface bugs (see 3.1.0 milestone)


The version is similar to the iOS version, in the fact that it has hardware encoding and ChromeCast integration.

As explained, the hardware encoding is done using QSV.

But it features also a large rework of the codebase and fixes a very large number of crashes.

Also, funnily enough, we've worked on the 8.1 version too, and we will push that one soon on the store. This includes SurfaceRT devices, even if Microsoft has forgotten them!

So VLC 3.1.0, UWP version will be out for:

  • Windows 10 Desktop (x86)
  • XBox One
  • Windows 10 Mobile (ARM)
  • Windows 8.1 Desktop (x86)
  • Windows 8.1 RT (ARM)

Once we fixed an issue, we might even do Windows Phone 8.1.

The Windows 10 versions are on the store today, and we're waiting for a deployment issue to be fixed to push the 8.1 versions!

(Note: if you are from Windows Central, you can contact me for more details)

Have fun!

July 18, 2018 10:06 PM

Welcome back!

After quite a bit of time far from the blog, I am back around here.

The biggest reason for this silence was that this was taking a lot of my time, but I had almost no positive feedback on those posts.

Let's see if we can do better this time :)

Here is a small cone, to make you more happy:

Large Cone

July 18, 2018 09:21 PM

July 13, 2018

Modern concurrency on Android with Kotlin

Geoffrey Métais

Current Java/Android concurrency framework leads to callback hells and blocking states because we do not have any other simple way to guarantee thread safety.

With coroutines, kotlin brings a very efficient and complete framework to manage concurrency in a more performant and simple way.

Suspending vs blocking

Coroutines do not replace threads, it’s more like a framework to manage it.
Its philosophy is to define an execution context which allows to wait for background operations to complete, without blocking the original thread.

The goal here is to avoid callbacks and make concurrency easier.

Basic usage

Very simple first example, we launch a coroutine in the Main context (main thread). In it, we retrieve an image from the IO one, and process it back in Main.

launch(Dispatchers.Main) {
    val image = withContext(Dispatchers.IO) { getImage() } // Get from IO context
    imageView.setImageBitmap(image) // Back on main thread

Staightforward code, like a single threaded function. And while getImage runs in IO dedicated threadpool, the main thread is free for any other job! withContext function suspends the current coroutine while its action (getImage()) is running. As soon as getImage() returns and main looper is available, coroutine resumes on main thread, and imageView.setImageBitmap(image) is called.

Second example, we now want 2 background works done to use them. We will use the async/await duo to make them run in parallel and use their result in main thread as soon as both are ready:

val job = launch(Dispatchers.Main) {
    val deferred1 = async(Dispatchers.Default) { getFirstValue() }
    val deferred2 = async(Dispatchers.IO) { getSecondValue() }
    useValues(deferred1.await(), deferred2.await())

job.join() // suspends current coroutine until job is done

async is similar to launch but returns a deferred (which is the Kotlin equivalent of Future), so we can get its result with await(). Called with no parameter, it runs in current scope default context.

And once again, the main thread is free while we are waiting for our 2 values.

As you can see, launch funtion returns a Job that can be used to wait for the operation to be over, with the join() function. It works like in any other language, except that it suspends the coroutine instead of blocking the thread.


Dispatching is a key notion with coroutines, it’s the action to ‘jump’ from a thread to another one.

Let’s look at our current java equivalent of Main dispatching, which is runOnUiThread:

public final void runOnUiThread(Runnable action) {
    if (Thread.currentThread() != mUiThread) {; // Dispatch
    } else {; // Immediate execution

Android implementation of Main context is a dispatcher based on a Handler. So this really is the matching implementation:

launch(Dispatchers.Main) { ... }
launch(Dispatchers.Main, CoroutineStart.UNDISPATCHED) { ... }

// Since kotlinx 0.26:
launch(Dispatchers.Main.immediate) { ... }

launch(Dispatchers.Main) posts a Runnable in a Handler, so its code execution is not immediate.
launch(Dispatchers.Main, CoroutineStart.UNDISPATCHED) will immediately execute its lambda expression in the current thread.

Dispatchers.Main guarantees that coroutine is dispatched on main thread when it resumes, and it uses a Handler as the native Android implementation to post in the application event loop.

Its actual implementation looks like:

val Main: HandlerDispatcher = HandlerContext(mainHandler, "Main")

To get a better understanding of Android dispatching, you can read this blog post on Understanding Android Core: Looper, Handler, and HandlerThread.

Coroutine context

A couroutine context (aka coroutine dispatcher) defines on which thread its code will execute, what to do in case of thrown exception and refers to a parent context, to propagate cancellation.

val job = Job()
val exceptionHandler = CoroutineExceptionHandler {
    coroutineContext, throwable -> whatever(throwable)

launch(Disaptchers.Default+exceptionHandler+job) { ... }

job.cancel() will cancel all coroutines that have job as a parent. And exceptionHandler will receive all thrown exceptions in these coroutines.


A coroutineScope makes errors handling easier:
If any child coroutine fails, the entire scope fails and all of children coroutines are cancelled.

In the async example, if the retrieval of a value failed, the other one continued then we would have a broken state to manage.
With a coroutineScope, useValues will be called only if both values retrieval succeeded. Also, if deferred2 fails, deferred1 is cancelled.

coroutineScope { 
    val deferred1 = async(Dispatchers.Default) { getFirstValue() }
    val deferred2 = async(Dispatchers.IO) { getSecondValue() }
    useValues(deferred1.await(), deferred2.await())

We also can “scope” an entire class to define its default CoroutineContext and leverage it.

Example of a class implementing CoroutineScope:

open class ScopedViewModel : ViewModel(), CoroutineScope {
    protected val job = Job()
    override val coroutineContext = Dispatchers.Main+job

    override fun onCleared() {

Launching coroutines in a CoroutineScope:

launch or async default dispatcher is now the current scope dispatcher. And we can still choose a different one the same way we did before.

launch {
    val foo = withContext(Dispatchers.IO) {  }
    // lambda runs within scope's CoroutineContext

launch(Dispatchers.Default) {
    // lambda runs in default threadpool.

Standalone coroutine launching (outside of any CoroutineScope):

GlobalScope.launch(Dispatchers.Main) {
    // lambda runs in main thread.


  • Coroutines limit Java interoperability
  • Confine mutablility to avoid locks
  • Coroutines are for threading waiting
    • Avoid I/O in Dispatchers.Default (and Main…)
    • Dispatchers.IO designed for this
  • Threads are expensive, so are single-thread contexts
  • Dispatchers.Default is based on a ForkJoinPool on Android 5+
  • Coroutines can be used via Channels

Callbacks and locks elimination with channels

Channel definition from JetBrain documentation:

A Channel is conceptually very similar to BlockingQueue. One key difference is that instead of a blocking put operation it has a suspending send (or a non-blocking offer), and instead of a blocking take operation it has a suspending receive.


Let’s start with a simple tool to use Channels, the Actor.

We already saw it in this blog with the DiffUtil kotlin implementation.

Actor is, yet again, very similar to Handler: we define a coroutine context (so, the tread where to execute actions) and it will execute it in a sequencial order.

Difference is it uses coroutines of course :), we can specify a capacity and executed code can suspend.

An actor will basically forward any order to a coroutine Channel. It will guaranty the order execution and confine operations in its context. It greatly helps to remove synchronize calls and keep all threads free!

protected val updateActor by lazy {
    actor<Update>(capacity = Channel.UNLIMITED) {
        for (update in channel) when (update) {
            Refresh -> updateList()
            is Filter -> filter.filter(update.query)
            is MediaUpdate -> updateItems(update.mediaList as List<T>)
            is MediaAddition -> addMedia( as T)
            is MediaListAddition -> addMedia(update.mediaList as List<T>)
            is MediaRemoval -> removeMedia( as T)
// usage
fun filter(query: String?) = updateActor.offer(Filter(query))
suspend fun filter(query: String?) = updateActor.send(Filter(query))

In this example, we take advantage of the Kotlin sealed classes feature to select which action to execute.

sealed class Update
object Refresh : Update()
class Filter(val query: String?) : Update()
class MediaAddition(val media: Media) : Update()

And all this actions will be queued, they will never run in parallel. That’s a good way to achieve mutability confinement.

Android lifecycle + Coroutines

Actors can be profitable for Android UI management too, they can ease tasks cancellation and prevent overloading of the main thread.

Let’s implement it and call job.cancel() when activity is destroyed.

class MyActivity : AppCompatActivity(), CoroutineScope {
    protected val job = SupervisorJob() // the instance of a Job for this activity
    override val coroutineContext = Dispatchers.Main.immediate+job

    override fun onDestroy() {
        job.cancel() // cancel the job when activity is destroyed

A SupervisorJob is similar to a regular Job with the only exception that cancellation is propagated only downwards.
So we do not cancel all coroutines in the Activity, when one fails.

A bit better, with an extension function, we can make this CoroutineContext accessible from any View of a CoroutineScope

val View.coroutineContext: CoroutineContext?
    get() = (context as? CoroutineScope)?.coroutineContext

We can now combine all this, setOnClick function creates a conflated actor to manage its onClick actions. In case of multiple clicks, intermediates actions will be ignored, preventing any ANR, and these actions will be executed in Activity’s scope. So it will be cancelled when Activity` is destroyed 😎

fun View.setOnClick(action: suspend () -> Unit) {
    // launch one actor as a parent of the context job
    val eventActor = (context as? CoroutineScope)?.actor<Unit>(
        capacity = Channel.CONFLATED) {
        for (event in channel) action()
    } ?:<Unit>(
        capacity = Channel.CONFLATED) {
        for (event in channel) action()
    // install a listener to activate this actor
    setOnClickListener { eventActor.offer(Unit) }

In this example, we set the Channel as Conflated to ignore events when we have too much of them. You can change it to Channel.UNLIMITED if you prefer to queue events without missing anyone of them, but still protect your app from ANR

We also can combine coroutines and Lifecycle frameworks to automate UI tasks cancellation:

val LifecycleOwner.untilDestroy: Job get() {
    val job = Job()

    lifecycle.addObserver(object: LifecycleObserver {
        fun onDestroy() { job.cancel() }

    return job
GlobalScope.launch(Dispatchers.Main, parent = untilDestroy) {
    /* amazing things happen here! */

Callbacks mitigation (Part 1)

Example of a callback based API use transformed thank to a Channel.

API works like this:

  1. requestBrowsing(url, listener) triggers the parsing of folder at url address.
  2. The listener receives onMediaAdded(media: Media) for each discovered media in this folder.
  3. listener.onBrowseEnd() is called once folder parsing is done.

Here is the old refresh function in VLC browser provider:

private val refreshList = mutableListOf<Media>()

fun refresh() = requestBrowsing(url, refreshListener)

private val refreshListener = object : EventListener{
    override fun onMediaAdded(media: Media) {
    override fun onBrowseEnd() {
        val list = refreshList.toMutableList()
        launch {
            dataset.value = list

How to improve this?

We create a channel, which will be initiated in refresh. Browser callbacks will now only forward media to this channel then close it.

Refresh function is now easier to understand. It sets the channel, calls the VLC browser then fills a list with the media and processes it.

Instead of the select or consumeEach functions, we can use for to wait for media and it will break once browserChannel is closed

private lateinit var browserChannel : Channel<Media>

override fun onMediaAdded(media: Media) {

override fun onBrowseEnd() {

suspend fun refresh() {
    browserChannel = Channel(Channel.UNLIMITED)
    val refreshList = mutableListOf<Media>()
    //Suspends at every iteration to wait for media
    for (media in browserChannel) refreshList.add(media)
    //Channel has been closed
    dataset.value = refreshList

Callbacks mitigation (Part 2): Retrofit

Second approach, we don’t use kotlinx-coroutines at all but the coroutine core framework.
Let’s see how coroutines really work!

retrofitSuspendCall function wraps a Retrofit Call request to make it a suspend function.
With suspendCoroutine we call the Call.enqueue method and suspend the coroutine. The provided callback will call continuation.resume(response) to resume the coroutine with the server response once received.

Then, we just have to bundle our Retrofit functions in retrofitSuspendCall to have a suspending functions returning the requests result.

suspend inline fun <reified T> retrofitSuspendCall(request: () -> Call<T>
) : Response<T> = suspendCoroutine { continuation ->
    request.invoke().enqueue(object : Callback<T> {
        override fun onResponse(call: Call<T>, response: Response<T>) {
        override fun onFailure(call: Call<T>, t: Throwable) {

suspend fun browse(path: String?) = retrofitSuspendCall {

// usage (within Main coroutine context)
livedata.value = Repo.browse(path)

This way, the network blocking call is done in Retrofit dedicated thread, coroutine is here to wait for the response, and in-app usage couldn’t be simpler!

This implementation is inspired by gildor/kotlin-coroutines-retrofit library, which makes it ready to use.
JakeWharton/retrofit2-kotlin-coroutines-adapter is also available with another implementation, for the same result.

To be continued

Channel framework can be used in many other ways, you can look at BroadcastChannel for more powerful implementations according to your needs.
We can also create channels with the Produce function.
It can also be useful for communication between UI components: an adapter can pass click events to its Fragment/Activity via a Channel or an Actor for example.

Related readings:

July 13, 2018 12:00 AM