Designing a Scalable Video Streaming Platform Like YouTube and Netflix

This guide breaks down a scalable video platform design (think YouTube or Netflix) step by step – from uploading a video to streaming it worldwide. It will clarify core requirements, explore each system component in order (upload, encoding, storage, CDN, playback, scaling), and even compare how YouTube and Netflix differ.

When you hit “play” on YouTube or Netflix, a stream starts almost instantly, even if millions of others are watching at the same time.

Behind this seamless experience lies a carefully engineered video streaming architecture designed to handle uploads, encode videos into multiple formats, store them reliably, and deliver them worldwide with minimal buffering.

In this blog, we’ll walk step by step through how such a system is built — starting from requirements, moving through upload and encoding pipelines, storage and CDN delivery, playback protocols, and finally, scalability strategies that keep these platforms running smoothly.

Let’s understand it together.

Functional and Non-Functional Requirements

Before diving into architecture, outline what the system should do (functional needs) and how it should perform (non-functional needs):

Functional Requirements

• Video Upload: Users can upload video files (supporting large, resumable uploads).

• Video Playback: Users can watch/stream videos on demand.

• Video Metadata: Store details like title, description, tags, etc., for each video.

• Search & Discovery: (Optional) Allow searching videos by title/tags and provide recommendations.

• User Interactions: (Optional) Track views, likes, comments, subscriptions, etc., for engagement.

Non-Functional Requirements

• Scalability & Availability: The platform must handle millions of users and videos, scaling horizontally without downtime.

• Low Latency: Videos should start playing quickly and stream smoothly (minimal buffering).

• Durability: Never lose uploaded videos – use reliable storage with backups (e.g., 99.999999999% durability on cloud storage).

• Global Reach: Deliver content worldwide with consistent performance (multi-region deployment, CDN for edge delivery).

• Cost Efficiency & Maintainability: Use cost-effective infrastructure (e.g., cloud storage vs. expensive media servers) and modular design for easy maintenance.

• Security: Secure uploads and playback (authenticated access, protect content, prevent unauthorized downloads).

With requirements in mind, let’s walk through the major components of a YouTube-like video streaming platform design in the order a video travels through the system:

Step 1: Video Upload

Uploading is the first step, and the goal is to ingest large video files reliably without overwhelming our servers.

The golden rule is to never route big uploads through the application server.

Instead, the client (web or mobile app) should upload the video file directly to cloud storage (e.g., an AWS S3 bucket or Google Cloud Storage) using a secure pre-signed URL.

How it Works

When a user hits “Upload,” the app requests an upload URL from the backend.

The backend generates a time-limited pre-signed URL pointing to blob storage, then returns it to the client.

The client then PUTs the video file straight to that URL. This way, the large file bypasses our application servers entirely, saving bandwidth and memory on the backend.

The backend only handles metadata (like video title, user info, etc.) and perhaps a small acknowledgment once the upload is complete.

To handle spotty networks, uploads use multipart resumable upload protocols.

The video is split into chunks (e.g. 5MB parts) and uploaded in parts; if a part fails, it can be retried without re-uploading everything.

This approach is how YouTube scales to thousands of simultaneous uploads – the heavy lifting is offloaded to cloud storage, and the app servers remain stateless and free to handle coordination.

Once the full video is stored in the cloud, the storage service can send a callback or event to our backend (e.g., an upload notification).

At this point, we mark the video metadata in our database and trigger the encoding pipeline asynchronously (more on that next).

We might also do a virus scan or validate file type in the background before further processing.

Step 2: Encoding and Transcoding

Raw uploaded videos come in all shapes and formats.

The platform needs to transcode each upload into a standard set of formats, resolutions, and bitrates for smooth streaming on any device.

In this stage, a video encoding service takes the original file and generates multiple encoded versions of the video.

What Does this Involve?

The encoding pipeline will create, for example, a 240p low-bitrate version, 480p SD, 720p HD, 1080p Full HD, etc., each using efficient codecs like H.264 or VP9.

YouTube and Netflix both use this approach – every uploaded video is converted into several files ranging from low quality (for slower connections or old devices) up to high quality (for fast internet and big screens).

This enables adaptive bitrate streaming (more on this in Playback), letting the client switch between quality levels as needed.

The encoding process may also produce thumbnails, generate subtitles or captions, and format the video into streaming-friendly containers.

Often, videos are segmented during encoding (especially for HLS/DASH streaming) – meaning the video is chopped into small chunks of a few seconds each while encoding, rather than one big .mp4 file.

To scale encoding, it’s handled asynchronously by worker nodes or a cluster of servers.

Once an upload is stored, a message (job) is queued (using something like Kafka or AWS SQS) for encoding.

A fleet of encoder workers pulls jobs from the queue and processes videos in parallel.

This decoupling means users don’t wait for encoding to finish; they can get a “Upload successful, processing video…” message while work happens in the background.

If an encoder fails or is slow, the job can retry or move to another worker (ensuring reliability).

This elasticity is crucial – during peak uploads, we can autoscale more encoder instances to keep up.

After transcoding, we end up with multiple versions of the video plus metadata about their locations. These encoded files are then stored back into the storage service (often in a structured folder or bucket path for that video) and associated with the video’s metadata in the database.

For example, our video metadata might have entries for “Video XYZ – 240p URL, 480p URL, 720p URL…” etc.

(Side note: To optimize costs, some platforms do on-demand encoding – e.g., encode 480p immediately for quick availability, but only encode 1080p when someone requests it. However, for simplicity, assume we encode a standard set of qualities upfront.)

Step 3: Storage (Media Servers and Object Storage)

Where do all these videos live?

We use object storage (like Amazon S3, Google Cloud Storage, or Azure Blob Storage) as our primary media storage.

Object storage is ideal for large binary files: it can scale practically infinitely, ensures durability (e.g., 11 nines durability means it’s exceedingly unlikely to lose data), and supports high throughput for streaming.

It also supports features like range requests (letting the video player request byte ranges of a file), which are useful for skipping to a certain part of a video without downloading it all.

In a YouTube-like system, the original video file and all encoded variants are stored in object storage.

We typically do not store video blobs in a traditional relational database or on a single server’s disk – that wouldn’t scale or be cost-effective.

Instead, the database (SQL or NoSQL) holds just metadata (video IDs, titles, which user uploaded, URLs or keys for the video files in storage, view counts, etc.), while the heavy video content sits in the object store.

What about “media servers”?

In early streaming systems, a media server might be a specialized server (or service like Wowza or Red5) that manages video streaming protocols.

In modern architectures, we largely replace that with just HTTP-based streaming from CDN (using HLS/DASH files).

So our “media server” is essentially the combination of object storage (for origin content) and CDN edge servers (for delivery).

No always-on streaming server process is needed because we serve video over standard HTTP (thanks to HLS/DASH). This simplifies scaling: any static file server or cloud storage link can deliver the video segments.

To ensure quick access globally, we might replicate storage across regions or use a multi-region bucket.

For instance, Amazon S3 can replicate data to buckets in Europe, Asia, etc., so that a user’s upload is copied closer to other users in those regions.

But even with replication, direct user access to the origin storage is inefficient, which leads us to the next step: content delivery networks.