March 26, 2023
  • We’re sharing how we’re enabling manufacturing and supply of AV1 for Fb Reels and Instagram Reels.
  • We consider AV1 is essentially the most viable codec for Meta for the approaching years. It presents larger high quality at a a lot decrease bit price in contrast with earlier generations of video codecs.
  • Meta has labored intently with the open supply neighborhood to optimize AV1 software program encoder and decoder implementations for real-world, global-scale deployment.

As individuals create, share, and devour an ever-increasing quantity of on-line movies, Meta is working to develop essentially the most bandwidth-efficient methods to transcode content material whereas sustaining affordable compute and energy consumption ranges. Selecting essentially the most acceptable video coding codecs — the algorithms for compressing and decompressing the file — is essential. Over the previous 20 years, researchers have developed video coding requirements with ever-higher compression effectivity, together with AVC, HEVC, and VVC, developed by MPEG/JVET, and VP9 and AV1, developed by Google and the Alliance for Open Media (AOM). Newer-generation normal usually can cut back the bandwidth by about 30 % to 50 % in contrast with its predecessor whereas sustaining related visible high quality. On the identical time, nonetheless, every new normal has consumed considerably extra power and compute than the final, whereas necessitating encoders that had been many occasions extra advanced.

We consider AV1 would be the most viable codec for Meta over the following a number of years. AV1 is the first-generation royalty-free video coding normal developed by AOM, of which Meta is a founding member. It delivers about 30 % higher coding effectivity than VP9 and HEVC — permitting individuals who use our apps to get pleasure from high-quality video at a lot decrease bandwidth, and enabling us to maximise storage effectivity and cut back egress site visitors, CDN prefetching/caching, and community congestion. AV1 additionally has a a lot richer characteristic set than different video coding requirements and may help most of Meta’s typical manufacturing usages. AV1 is royalty-free, and each the encoder and decoder implementations are open sourced, with very energetic growth and good help.

Over the previous few years, Meta has labored intently with the open supply neighborhood to optimize AV1 software program encoder and decoder implementations for real-world, global-scale deployment. Our purpose is to enhance playback from what we at the moment provide with AVC and VP9. We wish to make sure that as we roll out AV1, it delivers actual worth to the individuals who use our apps.

Discovering the precise AV1 encoders and decoders

A number of open supply and closed-source encoder implementations are prepared for manufacturing, all nearly as environment friendly because the AV1 reference encoder. In a paper, “Towards much better SVT-AV1 quality-cycles tradeoffs for VOD applications,” collectively revealed with Intel ultimately yr’s SPIE convention, we benchmarked a number of open supply encoders — together with x264, x265, libvp9, libaom, SVT AV1, and VVC reference encoder (vvenc) — for a video on demand (VOD) use case. The graph beneath illustrates the trade-off between encoder high quality (vertical axis) and complexity (horizontal axis). Each level on the graph corresponds to an encoder preset. The y-axis represents the typical BD-rate relative to libaom cpu-used=0; decrease values point out higher coding effectivity. The x-axis represents the encoding time in seconds in logarithmic scale.

A number of highlights from this graph:

  • SVT-AV1, the productization encoder for the AV1 coding normal, maintains constant efficiency throughout a variety of complexity ranges. With a complete of 13 presets, SVT-AV1 can cowl a complexity vary that extends from the upper high quality AV1 to the upper speeds AVC presets equivalent to greater than 1000x change in complexity. This complexity vary covers all open supply software program encoders utilized in manufacturing techniques.
  • At any given level on the x-axis, SVT-AV1 can maximize coding effectivity in contrast with another manufacturing encoder. For instance, the M12 preset has related complexity efficiency to the x264 veryfast preset, however M12 is about 30 % extra environment friendly.
  • At any given level on the y-axis, SVT-AV1 can maximize encoding pace in contrast with another manufacturing encoder. For instance, the M8 preset is about as environment friendly as libvp9 preset 0, however M8 is sort of 10 occasions quicker.

SVT-AV1 presents 13 presets, permitting a fine-grained trade-off between high quality and pace. Extra importantly, SVT-AV1 now features a “-fast-decode” choice, which accelerates software program decoding — with solely a slight drop in effectivity — by robotically limiting or disabling using AV1 coding instruments that aren’t software-decoder pleasant. SVT-AV1 additionally supplies thread administration parameters to steadiness density and pace — essential for large-scale manufacturing — probably enabling a one- or two-second delay for stay video streaming. Many parameters could be adjusted to enhance coding effectivity or to help sure manufacturing situations. Some AV1 coding instruments that had been proposed to be used instances in deployment, akin to reference body scaling, tremendous decision, movie grain synthesis, and change frames, are additionally supported in SVT-AV1.

Our greatest problem will likely be client-side decoding of AV1. Many {hardware} distributors, together with Intel and NVIDIA, have begun to help AV1 {hardware} decoding on PC. Nevertheless, we’re serving video primarily to cell phones, most of which don’t embrace AV1 {hardware} decoders. For now, we should rely totally on software program decoders. Two main open supply software program decoders are suitable with a number of platforms: dav1d was developed by VideoLAN and the open supply neighborhood and may function an app-level decoder, whereas Google’s libgav1 is built-in into the Android SDK.

After extensively benchmarking the decoders’ efficiency, specializing in aspects akin to useful resource necessities, crashes and responsiveness, and body drops, we determined to combine dav1d into the participant for each iOS and Android platforms. We have now been working intently with the open supply neighborhood to optimize dav1d’s efficiency. Within the final yr, we additionally labored with Ittiam to conduct a benchmark test on Android telephones. dav1d can help 720p30 real-time playback on a lot of the units in our pattern, reaching 1080p30 on sure mid-range and high-end fashions.

Some Android telephones, such because the Google Pixel 6 Professional and Samsung Galaxy S21, already help {hardware} AV1 decoding. Within the close to future, we count on {that a} rising variety of high-end Android fashions will help AV1 {hardware} decoding, with mid-tier units following ultimately.

Deploying AV1 encoding on Fb Reels and Instagram Reels

Early in 2022, we deployed AV1 encoding for Fb and Instagram Reels. When somebody uploads a video, the platform generates a number of bit-rate encodings tailor-made to the video’s projected watch time. To stop stalling attributable to adjustments in bandwidth, purchasers can choose the model that most closely fits their connection pace — a method known as adaptive bit rate (ABR) streaming. For movies with excessive projected watch time, we use superior ABR encoding based mostly on the convex hull dynamic optimizer algorithm. For every uploaded video, we produce a number of down-scaled variations and encode every with a number of quantization parameters (QPs) and Fixed Price Elements (CRFs). For instance, for a 1080p video, we’d create seven resolutions and 5 CRFs, for a complete of 35 encodings. After encoding, the system upscales decoded movies to the unique decision and calculates the standard rating.

Within the graph of price distortion (RD) curves beneath, the x-axis represents the encoding bit price and the y-axis the standard rating, expressed in FB-MOS items on a scale of 0 to 100.

From these 35 RD factors, we calculate the convex hull, a curve that connects the RD factors on the higher left boundary. (Theoretically, if we may use all potential encoding resolutions and CRFs to supply a a lot denser plot, any level on the convex hull would be the most optimum encoding choice for this video by way of decision and CRF worth.) As illustrated above, we will then choose the most effective encoding for supply based mostly on the goal high quality or bit price.

We have now simplified this difficult course of. In previous studies, we discovered that we may use the high-speed preset for first-pass encoding and to supply the convex hull, after which take a second cross to encode the chosen (decision, CRF) factors with the high-quality preset. Despite the fact that this method requires further encoding, it’s quicker as a result of the primary cross could be performed far more rapidly. (Coding effectivity drops solely barely.) This method works even when the primary and second passes use completely different encoders. For instance, we will use AVC or VP9 within the first cross and AV1 within the second. We are able to additionally leverage the {hardware} encoder in our internally designed ASICs to speed up this course of.

Ultimately, we selected a two-stage hybrid {hardware}/software program ABR encoding method. {Hardware} AVC encoding is triggered at video add time; for this stage, we retailer solely the standard and bit price data however not encoded bitstreams. When projected watch time of the video exceeds the edge, second stage encoding is triggered with software program AVC, VP9 or AV1 encoder based mostly on the chosen (decision, CRF) on the convex hull.

We are able to simply add AV1 as one of many second-stage encoders; it’s already deployed for Fb Reels. We have now carried out an analogous heuristic-based method for Instagram Reels. For one instance video proven within the graph above, three encoding households with AVC, VP9, and AV1 have been produced. Their RD curves intently comply with the convex hull from the first-stage encoding. For this specific video instance,  the best-quality AV1 encoding rivals these of the opposite two requirements, however with a bit price 65 % decrease than AVC’s and 48 % decrease than VP9’s. As well as, AV1 achieves the specified high quality inside a really slender bit price vary, so we will additional cut back compute and storage prices by producing fewer encodings throughout the second stage. In consequence, individuals who use our merchandise can get pleasure from high-quality video at a lot decrease bandwidth.

AV1 decoder integration and testing

It was comparatively straightforward to allow AV1 decoding and playback on the iOS units. After just some rounds of assessments, we began supply. To combine the dav1d decoder on iOS, we discovered that two to 4 threads would meet most of our manufacturing wants; any further threads would waste reminiscence and energy with out boosting efficiency.

dav1d has two modes: synchronous and asynchronous. In synchronous mode, dav1d decodes one body at a time however allows low-latency decoding for every body. In asynchronous mode, dav1d decodes a number of compressed frames in parallel, suspending rendering till all frames are decoded. In concept, asynchronous mode supplies larger throughput and quicker decoding. For now, we undertake synchronous mode on iOS because it matches the present participant stack, however we’re wanting into migrating to asynchronous mode sooner or later.

To help the decoding of 10-bit AV1-encoded HDR video, we constructed a single dav1d binary that helps each 8- and 10-bit decoding and ensures that shade data is preserved within the transcoding course of.

The Android platform introduced larger challenges. First, as a result of individuals have interaction with our apps on an unlimited variety of Android fashions, we needed to run native and large-scale A/B assessments on varied units to search out the optimum decoder configurations. To assist debug and triage issues from the AV1 decoder library, we added in depth logging that propagated again error messages from all through the participant stack. This essential step helped us rapidly establish and resolve points within the integration course of.

Second, as a result of we’re utilizing app degree software program decoders, we used the {hardware} VP9 decoder and software program AV1 decoder collectively when enjoying the identical video stream, to appropriately help combined codec manifest and in-stream ABR lane change. We wanted to ensure they interacted with the render engine appropriately.

We additionally wanted to help units with low efficiency and show decision. (This was not an issue with iPhones.) Though AV1 can encode high-resolution movies at a a lot decrease bit price than VP9, bit price discount is smaller for low-resolution movies. That makes it troublesome to indicate enchancment in top-line supply metrics for low-performance Android telephones. We responded by utilizing higher-quality encoding presets to spice up coding effectivity in low-resolution ABR lanes.

One other problem was that reminiscence allocation and thread creation elevated the decoding latency of the primary few video frames, prolonging the software program decoder begin time, delaying participant startup, and inflicting in-play stalls. This was most difficult with Reels, as a result of individuals usually scroll throughout a number of Reels movies in fast succession. To enhance scrolling efficiency, we prefetched a number of Reels movies earlier, earlier than they had been performed.

Earlier than we conduct a large-scale A/B supply take a look at, we’ve got to test whether or not the tip machine is highly effective sufficient for real-time decoding and playback of AV1 bitstreams. Nevertheless, there isn’t any straightforward method to classify Android cellphone efficiency. We can’t take a look at each mannequin that exists, as there are millions of them. And traits akin to core counts, chipset distributors, RAM dimension, and yr and mannequin should not ample indicators of functionality. We ultimately determined to run a small benchmarking take a look at to measure efficiency and provides every cellphone a efficiency rating. This benchmarking take a look at consisted of fundamental compute operations, together with Gaussian blur, reminiscence allocation, reminiscence copy, and 3D rendering. With this method, we may assign scores to any present or upcoming cell phones and group them based mostly on these numbers. Our A/B assessments then recognized the fashions that would help 720p, 1080p, and 10-bit HDR playback.

After the preliminary Android rollout, we began to allow AV1 {hardware} decoding for the few Android telephones that help it. We count on {hardware} decoding to enhance AV1 efficiency, and we plan to carry out large-scale assessments when a bigger variety of succesful telephones change into out there.

Newest supply standing

We began the AV1 supply for Fb Reels on iPhone in early 2022 and noticed the advantages inside the first week of the rollout. 

The next graph exhibits the week-over-week common playback FB-MOS for all Fb Reels movies performed on iPhones. Playback FB-MOS improved by about 0.6 factors after we deployed AV1. 

This second graph exhibits the typical bit price for all Fb Reels movies performed on iPhones. AV1 decreased the typical bit price by 12 %. 

This final graph exhibits the watch time of various codecs for Fb Reels on iPhone. AV1 watch time rose to about 70 % throughout the first week of rollout.

We have now continued to allow new options for iPhone, together with 1080p30 8-bit AV1 supply for iPhone 8 and past, 10-bit HDR supply as much as 1080p30 for fashions of iPhone X and past that help HDR show, and 1080p60 8-bit AV1 supply for iPhone 11 and past. AV1 encodes a excessive share of the Fb Reels and Instagram Reels movies watched on iPhones. We have now additionally enabled 8-bit AV1 supply to pick midrange to high-end Android telephones. The watch time share on Android for AV1 is comparatively small however rising.

What’s subsequent for AV1 at Meta?

AV1 delivers actual worth to the individuals who use our merchandise. It presents larger high quality at a a lot decrease bit price in contrast with earlier generations of video codecs. For instance, within the video beneath, there’s an apparent distinction in high quality between AVC, VP9, and AV1 at roughly the identical bit price.

Going ahead, we’ll proceed to broaden AV1 supply for Android telephones and allow {hardware} decoding in new units that help it.

For low-end Android telephones, it stays difficult to play again high-resolution AV1 bitstreams. To handle this, we’re at the moment experimenting with combined codec manifest help. On the server facet, the ABR supply algorithm generates a combined codec manifest that incorporates a number of video adaptation units with bitstreams encoded utilizing completely different codecs, akin to VP9 and AV1. It additionally specifies which AV1 and VP9 lanes the machine ought to select from based mostly on its efficiency rating. For instance, a low-end cellphone can play AV1 as much as 540p and change to VP9 for larger decision lanes. 

With increasingly more {hardware} distributors implementing AV1 decoders in cell SOCs, we count on the variety of AV1 succesful units to proceed to develop within the subsequent few years, permitting extra finish customers to get pleasure from the advantages of AV1.


This work is a collective effort by the Video Infra staff and Instagram staff at Meta, together with exterior companions, together with the Intel SVT staff, VideoLAN, Ittiam, Two Orioles, and the open supply neighborhood. The authors want to thank Jamie Chen, Syed Emran, Xinyu Jin, Ioannis Katsavounidis, Denise Noyes, Mohanish Penta, Nam Pham, Srinath Reddy, Shankar Regunathan, David Ronca, Zafar Shahid, Nidhi Singh, Yassir Solomah, Cosmin Stejerean, Wai Lun Tam, Hassene Tmar, and Haixiong Wang for his or her contributions and help.