Streaming Platform Backend

Part of the worlds-biggest-software-project initiative.

An open-source, cloud-native backend that unifies content transcoding, adaptive delivery, entitlement management, recommendation, and analytics into a single deployable platform -- replacing the patchwork of proprietary services that streaming operators currently assemble by hand.

Streaming Platform Backend provides the core server-side infrastructure needed to run a video or audio streaming service. It targets engineering teams at media companies, niche content networks (sports, education, enterprise video), and startups that need production-grade streaming without locking into a single cloud vendor or stitching together half a dozen SaaS products. The project addresses the integration complexity that causes cost overruns and scalability crises when organisations attempt to build these systems independently.

Why Streaming Platform Backend?

No open-source all-in-one exists. Kaltura is the closest, but it requires heavy infrastructure management, carries an AGPL licence that complicates commercial use, and its recommendation engine is basic. Every other examined solution is proprietary SaaS.
Vendor lock-in is the norm. AWS Media Services dominates but provides only infrastructure primitives -- no entitlement management, no recommendation, no catalog, no ad integration. Teams must build or buy each missing layer separately and maintain the glue code.
Incumbents leave critical gaps unfilled. None of the ten solutions examined handle territorial rights restrictions, content windowing, or blackout periods. Unified analytics that correlate viewer drop-off with entitlement changes or recommendation quality do not exist in any current product.
Costs are opaque. Most platforms obscure the relationship between transcoding, storage, and delivery costs. Per-title budget caps and transparent cost breakdowns are unavailable outside of Cloudflare's simple per-minute model.
AI-driven personalisation requires ML expertise. Every platform with a recommendation engine either demands integration with external ML infrastructure or deep machine-learning knowledge. A pre-trained, plug-and-play recommendation module would lower the barrier for smaller operators.

Key Features

Content Ingestion and Transcoding

GPU-accelerated transcoding pipeline supporting H.264, HEVC, VP9, and AV1
Per-title encoding optimisation that adapts the bitrate ladder to content characteristics rather than using fixed profiles
HLS and MPEG-DASH adaptive delivery with multi-device targeting
Batch and live transcoding with job-based status callbacks

Content Delivery and DRM

Global CDN integration (CloudFront, Akamai, Fastly, Cloudflare) with origin shielding to reduce origin load
Multi-CDN routing for redundancy and cost optimisation
Widevine, FairPlay, and PlayReady DRM with KMS integration
Signed URLs and token-based playback authorisation with sub-50ms validation latency

Entitlement and Subscription Management

Subscription tiers, free-trial windows, pay-per-view, and geographic restrictions
Device-limit enforcement and real-time token issuance
Edge-cached entitlement validation for low-latency playback start
Third-party payment processor integration

Recommendation and Discovery

Collaborative filtering and content-based models trained on viewing history, completion rates, ratings, and search behaviour
A/B testing framework for algorithm variants
Recommendation cold-start handling using content features (genre, duration, cast)
Structured catalog API with search, browse, and editorial curation

Advertising and Monetisation

Server-side ad insertion (SSAI) for ad-supported tiers
VAST/VMAP compliance with audience targeting signal integration
Support for SVOD, AVOD, and TVOD monetisation models

Analytics Pipeline

Real-time event ingestion (play, pause, seek, buffering, completion) from client SDKs
QoE monitoring, engagement reporting, and audience segmentation
Unified analytics correlating engagement, entitlements, and recommendation quality
Data pipeline feeding recommendation model retraining

Multi-Platform Client SDKs

Standardised API surface for web, iOS, Android, Smart TV, and connected devices
White-label player customisation
Multi-tenant support for independent organisations on a shared deployment

AI-Native Advantage

Current streaming platforms rely on heuristics for bitrate ladder selection, rules-based ad placement, and manual content moderation. An AI-native approach replaces these with models that predict optimal bitrates from content features (action scenes vs. talking heads), identify viewers at risk of churn from viewing patterns, optimise ad timing based on viewer attention signals, and detect problematic content in live streams using computer vision and NLP. The unified data backbone -- shared across recommendation, ad-tech, CMS, and analytics -- enables cohesive personalisation across all touchpoints rather than isolated model outputs.

Tech Stack & Deployment

Protocols: HLS, MPEG-DASH, CMAF for adaptive streaming; RTMP for live ingest; VAST/VMAP for ad integration
DRM: Widevine, FairPlay, PlayReady with external KMS integration
Deployment: Cloud-native architecture targeting Kubernetes; self-hosted, cloud, or hybrid deployment modes
Edge: CDN integration with origin shielding; edge-cached entitlement validation
APIs: RESTful APIs for content ingest, delivery, analytics, and catalog management; webhook callbacks for asynchronous events
Transcoding: GPU-accelerated encoding with per-title optimisation; FFmpeg as a foundation component (LGPL/GPL -- requires careful licensing review for proprietary use)

Market Context

The global video streaming market was valued at approximately USD 670 billion in 2024 with compound annual growth above 15%. Beyond consumer entertainment, enterprise video, niche content networks, and educational platforms represent underserved segments where a packaged streaming backend could replace expensive custom builds. Incumbent pricing ranges from opaque per-minute models (Cloudflare Stream) to enterprise contracts that bundle infrastructure lock-in (AWS Media Services, Mux). Primary buyers are engineering teams at mid-size media companies and platform startups seeking to move faster without committing to a single vendor.

Project Status

This project is in the research and specification phase. Contributions, feedback, and domain expertise are welcome.

Contributing

We welcome contributions from developers, domain experts, and potential users. See CONTRIBUTING.md for guidelines.

Important: All contributions must be your own original work or clearly attributed open-source material with a compatible licence. Copyright infringement and licence violations will not be tolerated and will result in immediate removal of the offending contribution. If you are unsure whether a piece of code, text, or other material is safe to contribute, open an issue and ask before submitting.

Licence

Licence to be determined. See discussion for context.

Note: The project may incorporate FFmpeg (LGPL/GPL) and should consider Kaltura's AGPL licensing model as a reference. Video codec patents (H.264, HEVC) controlled by patent pools may require licensing in some jurisdictions. Legal review is recommended before production adoption.