No More Human Needed to Press the Stream Button — How to Create Unmanned Streaming on YouTube/Twitch

Creating unmanned streaming on YouTube or Twitch can be achieved through the use of specialized streaming software and software that utilizes AI avatars. Though the terminology surrounding RTMP, HLS, WebRTC, and SRT can be overwhelming, this article aims to simplify the process by outlining the challenges, causes, and solutions involved in automating the stream process. Here's the step-by-step sequence:

1. When the program's designated start time is reached: Utilize APIs to create a broadcast, bind the stream to RTMP, and start the GPU Pod to send out the video.

2. Transition to live: Once these steps are complete, the stream goes live.

3. Closing message when the time is up: After the specified time has elapsed, the stream is automatically ended, the GPU Pod is destroyed, and the archive (VOD) remains.

This unmanned streaming process can be applied to both YouTube and Twitch platforms. The article will first provide an overview of the general layout, followed by an in-depth look at specifics such as simultaneous streaming, automation using APIs, potential issues with automatic start triggers, handling stream termination, and latency.

To understand the structural role division, streaming is broken down into three segments: [1. Production], [2. Ingest], and [3. Delivery]. Each segment utilizes distinct technologies and requires different approaches. While much confusion arises from conflating these segments, recognizing their individual functions is crucial to avoiding complications.

The production segment encompasses flexibility, real-time capabilities provided by software like OBS, browsers, cameras, and ffmpeg. The ingest segment is responsible for reliability and platform compatibility, where RTMP, along with other protocols such as SRT and WHIP, come into play. Delivery focuses on scalability and CDN distribution, utilizing variants of HLS.

RTMP is still the standard for ingest due to its wide support among major platforms and its simplicity. While newer protocols like SRT and RIST offer improved loss resilience and WebRTC-based ingest (WHIP) is emerging, RTMP remains the most widely supported option. Importantly, RTMP lacks failure reporting, making external verification of the receiver's status essential to distinguish success from failure.

WebRTC is designed for real-time, bidirectional communication, but it is not the primary choice for large-scale live streaming due to its peer-to-peer structure and lack of CDN distribution integration. Instead, WebRTC is used internally for tasks like AI voice generation and avatar lip-syncing, with the output being routed through RTMP for delivery to the intended platforms.

ffmpeg, a versatile video conversion tool, serves three simultaneous roles in streaming pipelines: encoding raw video and audio, multiplexing video and audio into a single stream, and distributing encoded packets to multiple outputs. Encoding happens only once, while tee multiplexing duplicates the encoded stream for simultaneous streaming to YouTube and Twitch.

By employing the right tool for each segment—ffmpeg for encoding, RTMP for ingest, and WebRTC for production—a more efficient streaming process can be achieved. The utilization of ffmpeg's tee multiplexer is crucial for distributing the encoded stream across multiple outputs, such as YouTube and Twitch, without significantly increasing the CPU load.