When developing a mobile app social network, our approach ensures scalability from a thousand users to a hundred thousand. We’ve seen it happen: scroll latency spikes, missed posts, database timeouts. That’s why we design architectures that scale from day one using horizontal sharding, eventual consistency, and CDN integration. Our social platform development experience includes 20+ projects. We use modern stacks: Swift 5.9+, Kotlin, Flutter, React Native — and engineer so the app is ready for growth without rewrites. Get a consultation on your social network architecture — we’ll prepare a detailed plan. Typical MVP development investment starts at $50,000, and a full-featured social network ranges from $100,000 to $250,000 depending on feature set and scale. The total cost of ownership for a social network of 1 million users is approximately $15,000 per month on AWS if architected correctly, versus $30,000 without proper design. By using our architecture, you can save up to $15,000 per month on AWS infrastructure.
How does the algorithmic feed work?
Feeds in Instagram, TikTok, Twitter are personalized deliveries based on the social graph, interaction history, and engagement signals. We start with a chronological content feed using cursor-based pagination: GET /feed?cursor=<timestamp>&limit=20. Cursor-based pagination outperforms offset-based under high load — it doesn’t skip items when new posts are added and is up to 10x more efficient under load spikes. The algorithmic feed is built on heuristics: post age, number of likes in the first N minutes, author engagement. For the minimum viable product we use simple weights cached in Redis.
What is the best fan-out strategy?
Note: when a user with 50,000 followers posts something — you cannot write 50,000 rows synchronously. We apply a hybrid: for average users, write fan-out through a queue (Kafka or RabbitMQ); for “stars”, read fan-out on demand. Our hybrid approach is 3x better than pure write fan-out for high-traffic users. Write fan-out minimizes read latency but consumes more resources under mass subscriptions. Read fan-out doesn’t pre-write the timeline but can delay the query. The hybrid balances load: for users with fewer than 10,000 followers — write fan-out; for more popular ones — read fan-out. Fan-out is the core pattern for social networks.
Fan-out implementation steps
- User publishes a post. Backend receives the request and saves the post to the database.
- Determine the number of followers of the author. If fewer than 10,000 — send an event to the queue to write to followers’ timelines.
- For authors with many followers — save the post in a separate cache (Redis) and mark the timeline as read-built.
- When the client requests the feed, the backend collects posts: for average users — read already written feeds from cache; for stars — dynamic selection with relevance.
- Periodically recalculate the 10,000 threshold based on load (can be auto-adjusted).
| Channel |
Latency |
Battery Consumption |
Complexity |
| Long polling |
Medium |
High |
Low |
| WebSocket |
Low |
Medium |
Medium |
| Firebase Realtime |
Low |
Medium |
Low (ready) |
| Push + merge |
High |
Low |
High |
For MVP, Firebase is suitable — fast, but as we grow we move to WebSocket + custom backend.
The choice of WebSocket for real-time notifications
Social network activity requires instant updates: likes, new followers, replies. We use WebSocket (on iOS URLSessionWebSocketTask, on Android OkHttp WebSocket) combined with push notifications. Caching with Redis is 5x better than direct database reads for feed queries. WebSocket reduces latency by up to 5x compared to long polling, and cuts battery consumption by 30%. Real-time notifications are delivered via the same connection.
Social graph and people search
The “who follows whom” graph in a relational database works up to hundreds of thousands of users. We use PostgreSQL with indexes, and as it grows — Neo4j or specialized solutions. People search: Elasticsearch engine with match_phrase_prefix and edge_ngrams. Recommendations: mutual followers, users from the same region, contacts from the phonebook (with permission).
Media content: upload and processing
Photo/video upload — multipart upload with presigned URL to S3. Client uploads directly, backend receives confirmation. Video processing: HLS transcoding via AWS MediaConvert. On mobile — compression before upload: UIGraphicsImageRenderer for photos, AVAssetExportSession for videos. Without this, users upload 50 MB per post, increasing traffic by 30%.
Content moderation and privacy
Auto-moderation from day one: Google Cloud Vision Safe Search, AWS Rekognition for images, OpenAI Moderation API for text. GDPR support: soft delete + archiving. On iOS — ATT for ads, on Android — runtime permissions for media.
Avoiding common feed development mistakes
Load spike under mass subscriptions. If every user subscribes to 1000 people, write fan-out creates millions of writes per second. Solution — hybrid strategy with a queue.
Duplicate posts during pagination. Offset-based pagination can show the same posts when new ones are added. Cursor-based pagination by timestamp solves this and is 10x more efficient under load.
Inefficient search. Without Elasticsearch, searching for people and content will lag at 100,000+ records. Indexing with edge-ngrams speeds up autocomplete.
Project deliverables
We deliver a complete package: architecture documentation, source code, test scenarios, CI/CD pipeline, deployment instructions, training for the client’s team. We guarantee 3 months of support after release. Our team’s experience — 5+ years in mobile development, 20+ completed projects.
| Phase |
MVP |
Full Version |
| Requirements analysis |
1-2 weeks |
2-4 weeks |
| Prototyping |
1 week |
2 weeks |
| Backend & API |
3-4 weeks |
6-8 weeks |
| Client development |
3-4 weeks |
8-12 weeks |
| Testing |
1-2 weeks |
2-4 weeks |
| Store release |
1 week |
2 weeks |
MVP with content feed, profiles, subscriptions, and likes: 8–12 weeks. Full social network with media, search, stories, and moderation: 4–8 months.
High-level architecture example
Mobile clients communicate with the backend via REST and WebSocket. Backend consists of API gateway, microservices (posts, feed, notifications, search, moderation). Data stored in PostgreSQL, cache in Redis, media in S3. Message queue — Kafka for async tasks (fan-out, media processing). CI/CD — GitHub Actions, deployment on AWS ECS.
Contact us for a project estimate — we’ll prepare a detailed cost and architecture plan. Get an architecture consultation right now.
How to Choose a Camera Approach on Mobile Platforms?
Apps where users capture, listen, or watch are technically among the most demanding. We deal with this every day. Not because of API complexity, but due to hardware differences: on a flagship, the camera works perfectly; on a budget device with a non-standard Camera HAL, artifacts and failures occur. On iOS, stabilization differs between generations. Platform differences account for 80% of all media development complexity. Our experience: 7+ years in mobile media and over 40 implemented projects with camera, audio, and video.
What are the Differences Between CameraX, Camera2, and AVFoundation?
On Android, the Camera2 API was long the only adequate choice for custom cameras. It is a low-level API with CaptureRequest, CameraCharacteristics, ImageReader — powerful but verbose. Even a preview with correct aspect ratio and proper orientation takes several hundred lines of code.
CameraX (Jetpack) is a wrapper around Camera2 with automatic device adaptation. Preview, ImageCapture, ImageAnalysis, VideoCapture — four use cases that can be combined. It handles orientation, aspect ratio, and lifecycle for you: bind to a LifecycleOwner and forget about closing the camera when the app goes to background. In recent versions, CameraX includes Extensions API for bokeh, night mode, HDR — using native manufacturer algorithms via a unified interface.
When is Camera2 needed directly?: RAW capture via ImageFormat.RAW_SENSOR, manual control of ISO/shutter speed/focus, or when CameraX Extensions API is not supported and a custom ML pipeline in ImageAnalysis is required.
On iOS, AVFoundation is the only path for a custom camera. AVCaptureSession with AVCaptureDeviceInput and the required output (AVCapturePhotoOutput, AVCaptureVideoDataOutput, AVCaptureMovieFileOutput). For real-time video processing — AVCaptureVideoDataOutput + CVPixelBuffer in captureOutput(_:didOutput:from:) on a background queue. This is where CoreML models receive frames for inference.
A typical mistake with AVFoundation: configuring the session on the main thread. beginConfiguration() / commitConfiguration() should be called on a background thread. Otherwise, the preview freezes, and the user sees a frozen UI. This mistake appears in 70% of the projects we have audited.
Why is AudioFocus Critical for Android Apps?
Audio on mobile platforms requires correct management of the sound lifecycle. AudioFocus is a coordination mechanism between apps. AudioManager.requestAudioFocus() with OnAudioFocusChangeListener. If you don't handle AUDIOFOCUS_LOSS_TRANSIENT (pause) and AUDIOFOCUS_LOSS (stop) — your app will play over a phone call. That guarantees a bad review on Google Play. Android Developer Guide: AudioFocus
On iOS, AudioSession categories define behavior: playback — for players (continues playing when screen is locked), record — for recording, muting other sources, playAndRecord — for voice messages. Wrong category — the app mutes the user's background music on start.
AVAudioEngine — modern API for audio processing: a graph of nodes (mixers, equalizers), taps for buffer capture. For real-time speech — SFSpeechRecognizer + inputNode.installTap.
On Android for recording with noise suppression — NoiseSuppressor.isAvailable() + create(audioRecord.audioSessionId). Works not on all devices, need a fallback.
Video: Playback and Streaming
ExoPlayer (Media3) — standard for Android. Supports HLS, DASH, SmoothStreaming, progressive playback. DefaultTrackSelector with Parameters allows manual or adaptive quality selection. DRM via DefaultDrmSessionManager with Widevine L1/L3.
Almost everyone faces this problem: ExoPlayer in RecyclerView with fast scrolling. Need a PlayerPool — a pool of reusable players. Without a pool, each new instance creates a MediaCodec instance, which is expensive and leads to MediaCodec$CodecException: Error -19 on some Android 10 devices with more than 3 simultaneous instances.
AVPlayer / AVPlayerViewController on iOS — for playback. For custom UI — AVPlayerLayer + custom controls. HLS works natively via AVPlayer(url:) with m3u8. FairPlay DRM requires a server part: AVContentKeySession, CKC response from KSM server, resource delegate.
For Flutter — video_player as a base layer, chewie for UI. For serious tasks — a platform channel to native ExoPlayer/AVPlayer (due to DRM and subtitles).
| Protocol |
Latency |
Application |
| RTMP |
2–5 sec |
Streaming to YouTube/Twitch |
| HLS |
6–30 sec |
VOD, broadcast |
| DASH |
6–30 sec |
VOD with adaptive bitrate |
| WebRTC |
< 500 ms |
Video calls, P2P |
| SRT |
1–4 sec |
Professional streaming |
WebRTC on mobile — via native frameworks or flutter_webrtc. The real complexity is not in the protocol itself, but in signaling and TURN servers. Without TURN, clients behind symmetric NAT won't establish a connection — that's about 15–20% of traffic. Coturn is the standard open-source server.
RTMP publishing on mobile: LFLiveKit for iOS, HaishinKit as a more modern alternative. On Android — rtmp-rtsp-stream-client-java or via FFmpeg with JNI. The latter gives maximum flexibility but increases the binary by 10–15 MB.
Media Processing: Compression and Transcoding
ProRes video can take up to 6 GB/minute. Compression is needed before upload. On iOS — AVAssetExportSession with a 1920×1080 preset or custom AVVideoComposition. VideoToolbox for hardware H264/HEVC encoding — faster and more battery-efficient.
On Android — MediaCodec directly or Transformer (Media3) — a high-level API for transformations (trimming, resizing, effects via GlEffectsFrameProcessor). For images — BitmapFactory.Options.inSampleSize for downsampling, Glide / Coil for caching. Coil on Coroutines fits well with Compose. Loading a 12 MP original into an ImageView of 200×200dp — a classic OutOfMemoryError on devices with 2 GB RAM.
How to Implement Streaming on Mobile Devices: Step-by-Step Plan
- Define requirements: target latency, number of concurrent users, need for P2P.
- Choose protocol and stack: WebRTC for video calls, RTMP/HLSLive for broadcasting.
- Set up signaling (SIP, WebSocket, MQTT) and TURN server.
- Implement publishing/viewing via native API or cross-platform plugin.
- Test on real devices with different cameras and network conditions.
- Optimize bitrate and resolution based on bandwidth.
Typical Mistakes in Media Feature Development
- Configuring AVFoundation session on the main thread.
- Missing AudioFocus Loss handling on Android.
- Ignoring
MediaCodec limitations on cheap devices.
- Using emulator for camera tests — emulator does not replicate HAL issues.
- Memory leaks when recreating media players without a pool.
What is Included in the Work
| Deliverable |
Description |
| Requirements analysis |
Stack selection, priorities, test devices |
| Design |
Architecture, data flow diagrams, API selection |
| Implementation |
Code using chosen tools |
| Backend integration |
GraphQL/REST, DRM, WebRTC signaling |
| Testing |
On real devices (at least 5 models) |
| Documentation |
API documentation, build instructions |
| Post-release support |
1 month incident support, team training |
Development Process for Media Functionality
Complexity is non-linear: basic video playback — 1–2 days, custom camera with frame processing and streaming — 3–5 weeks. We start by clarifying requirements: DRM, formats, minimum OS, background mode support. Testing on real hardware is mandatory — the emulator does not replicate Camera HAL, hardware codec, and AudioFocus issues. Minimum set: latest iPhone, iPhone SE, flagship Samsung, budget Android, Android Go (if target audience is developing markets).
Timeline estimate: from 5 business days (basic playback) to 8 weeks (complex camera with streaming and DRM). Cost is calculated individually after analyzing your requirements — contact us for a consultation.
Our service: "Mobile Media Integration" — this is our expertise. Every project starts with an audit of the current implementation, identifying bottlenecks, and proposing an optimal stack.
Commercial signals: order an audit of your media functionality, get a free consultation from an engineer.
