Hear from Paresh Rajwat, the Vice President and Head of Product for Facebook Audio, Video, and Music.

Serving Live Videos with high reliability is challenging, not only from the perspective of deploying improvements on top of a distributed system but also from the perspective of defining correct measurements to capture reliability gaps that matter to users. Facebook’s Live platform spans from the ingest endpoints where creators upload their streams to services that are in charge of transcoding and generating multiple video renditions, as well as services executing the delivery on egress stack and CDN endpoints that serve broadcasts to the millions of viewers. All of these pipelines have to do the work in orchestration with realtime guarantees and even a single failure can be severe for our users. In this talk, Petar will show how we evolved our thinking about key reliability metrics over the time, and how we derive actionable insights to make Facebook Live as reliable as possible from the user’s perspective.

Reducing end-to-end streaming latency is critical for HTTP-based live video streaming. There are currently two new technologies in this domain: Low-Latency HTTP Live Streaming (LL-HLS) and Low-Latency Dynamic Adaptive Streaming over HTTP (LL-DASH). Several streaming players (Apple's AVPlayer, Google's Shaka Player, HLS.js, DASH.js, etc.), streaming encoding and packaging tools (Apple's HTTP Live Streaming Tools, FFmpeg, GPAC, etc.), have recently added support for these formats. There are also several live LL-HLS and LL-DASH reference/demo streams available, showcasing the capabilities of these technologies and helping the implementation community to adopt them. However, how well such systems perform in real-world deployment scenarios is not well known. Specifically, not much is known about the performance of such low-latency systems in delivery to mobile devices, connected over rapidly changing and often highly loaded wireless cellular networks. In this talk, Yuriy will review some results obtained in this direction by the Brightcove Research team. Our study consists of a series of live LL-HLS and LL-DASH streaming experiments. For each experiment, we use the same video content, encoder, encoding profiles, and identical network conditions emulated by running traces of the existing cellular networks (Verizon 4G LTE, T-Mobile 4G LTE, etc.). Several key performance metrics are captured and reported in each experiment. We measure the average streaming bitrate, the overall amounts of media data downloaded, the intensity of data requests, streaming latency, playback speed variations, buffering, stream switching statistics, etc. These results are subsequently analyzed and used to characterize some typical limits of LL-HLS and LL-DASH-based systems and differences between them.

Two years ago, iStreamPlanet set out to build a cloud-native software transcoder with the reliability and feature set to support some of the highest profile live channels and events in the world. Some of our goals included: 4+ 9’s of uptime, as little C/C++ code as possible; the ability to run 1000’s of live channels without human oversight; support for advanced features like SCTE-35 Signaling & Segmentation, Hitless merging of redundant video sources, and Dolby Digital Plus Surround Sound encoding; and the ability to update the software on running channels without customer impact. We’ll explain how we leveraged the power of Go, Kubernetes, and a mix of commercial and OSS components to make that vision a reality. The result powers the OTT distribution over 1000 live linear TV channels running 24/7, and has been used to deliver the streams for March Madness 2021, the Tokyo Olympics games, the UEFA Champions League, and more.

Understanding video content has been a focus for video-sharing platforms. It is one of the most important driving forces for the growth in distribution, discovery, user experience and monetization. Instream video understanding is the technology area where we analyze and utilize finer granularity video signals in the spatial and the temporal domains. The fine-grained spatial and temporal signals can be used for consumer facing products or used as signals for downstream models and pipelines. For example, in the spatial domain, we identify the salient regions inside each frame, which enables a system to automatically reframe a horizontal (landscape) video into a vertical (portrait) one. In the temporal domain, we identify the highlight score of each frame, which enables us to identify the highlight moments inside a video and create a video trailer.

Malicious synthetic media – both deepfakes and cheapfakes – are rising in prevalence and importance. End users are rapidly losing trust in media, and their ability to tell authentic media from inauthentic has greatly diminished. This talk will cover the dangers of synthetic media in the video ecosystem, approaches to combating malicious synthetic media, and an overview of how Media Provenance techniques, as developed by the Coalition for Content Authenticity and provenance, can be utilized to provide media trust signals to end users.

A/B testing on video isn’t just about tweaking recommendations or picking the perfect thumbnail. Every aspect of video benefits from rapid experimentation including the infrastructure — streaming algorithms, codecs, bitrates, caching strategies, network congestion control algorithms. Join us for a behind the scenes tour from practitioners on how experimentation helps learn what users want. We’ll share experiment results that confounded us, share insight about infrastructure to enable experimentation for everything and bust common myths that cause people to think experimentation isn’t for them. If you care about making video better, there’ll be something for you!