Image Upload Pipeline for Mobile Chat

User taps the attach button, selects a photo from the gallery — and waits. If nothing happens, they tap again. This is a common scenario when image upload is implemented without a queue and progress indicator. The result: duplicates in chat, app crash on a slow network, and a one-star review. Our experience shows that even simple photo sending requires attention to detail: a proper pipeline eliminates 90% of performance and UX issues. Contact us to implement reliable image sending in your app.

Why a Naïve Implementation Leads to Errors

The most common mistake is uploading the original image directly. Modern smartphone cameras produce 4–12 MB photos. Sending such a file via multipart/form-data without prior compression means: long wait times, UI freeze on the main thread (if compression is done there), and duplicates when the user retries after a timeout.

On iOS, a typical implementation uses PHPickerViewController (iOS 14+), which provides an NSItemProvider from which you asynchronously obtain a UIImage. If you do this synchronously in a completion handler and immediately call ImageIO for resizing, the UI will freeze for ~300 ms on an iPhone 12, and even longer on an iPhone SE 2nd gen. The correct approach: load data in the background via loadObject(ofClass:), then dispatch to DispatchQueue.global(qos:.userInitiated) for compression using vImage or UIGraphicsImageRenderer with a target size suitable for the receiver's screen. Apple Developer Documentation

On Android, a similar problem occurs with ActivityResultContracts.GetContent(); decoding a Bitmap on the main thread via BitmapFactory.decodeStream() without inSampleSize leads to an OutOfMemoryError on devices with 2 GB RAM when selecting multiple photos in a row.

How We Build a Reliable Upload Pipeline

Our complete pipeline includes the following steps:

1. Selection and validation. On iOS — PHPickerViewController with filter: .images, limit via selectionLimit. We check MIME type using UTType before loading data. On Android — PhotoPicker API (Android 13+) or Intent(Intent.ACTION_PICK) for older versions; validation via ContentResolver.getType(). In React Native we use react-native-image-picker with mediaType: 'photo'.

2. Compression. Target: no more than 1 MB for chat thumbnails. On iOS: UIGraphicsImageRenderer with target size 1280×1280, jpegData(compressionQuality: 0.75). On Android: Bitmap.createScaledBitmap() + compress(Bitmap.CompressFormat.JPEG, 80, outputStream). In Flutter we use flutter_image_compress — it calls native codec, so it does not block the Dart isolate.

3. Upload with progress. Multipart upload via URLSession.uploadTask(with:from:) on iOS with delegate urlSession(_:task:didSendBodyData:) for progress. On Android — OkHttp with RequestBody.create() and a custom CountingRequestBody. In React Native, axios with onUploadProgress is convenient, but note: the progress event fires on the JS thread, so state updates must be debounced.

4. Optimistic UI. Show a thumbnail immediately after selection, with a "uploading" status while the upload proceeds. If the request fails, do not delete the message; show a retry button instead. Each message must have a local localId and a status (pending / sent / failed).

5. Full-screen preview. On iOS — UIScrollView + UIImageView with pinch-to-zoom via UIPinchGestureRecognizer. Lazy loading of the original when opening, using SDWebImage or Kingfisher. On Android — PhotoView library or ZoomableImageView from coil + accompanist.

Contact us to implement this pipeline in your app today.

Storage and CDN

Images are not stored in the database. We upload to an S3-compatible storage (AWS S3, Cloudflare R2, MinIO) and write only the URL to the chat. For thumbnails, we generate them server-side via Lambda/Cloud Function on upload — this frees the client from re-compressing when displaying the message list.

Presigned URLs with a TTL of 1–24 hours are mandatory for private chats. On the client, we cache via NSCache (iOS) or DiskLruCache (Android). We use a CDN with edge caching for fast delivery even to regions with high latency.

Process and Timeline

Basic implementation (selection, compression, upload, thumbnail, full-screen preview) — 2–3 days with a ready backend. If you need multi-select (up to 10 photos), an upload queue with pause/resume, and GIF support — add 1–2 days. Cost is determined individually after analyzing requirements.

What's Included

• API and integration documentation
• Source code of the pipeline with comments
• CDN and presigned URL setup
• Integration with your existing backend (REST/GraphQL)
• Testing on slow networks and edge cases
• Support during App Store and Google Play release

Common Mistakes and Their Solutions

Get a free consultation on implementing image sending — just contact us.

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).

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

1. Define requirements: target latency, number of concurrent users, need for P2P.
2. Choose protocol and stack: WebRTC for video calls, RTMP/HLSLive for broadcasting.
3. Set up signaling (SIP, WebSocket, MQTT) and TURN server.
4. Implement publishing/viewing via native API or cross-platform plugin.
5. Test on real devices with different cameras and network conditions.
6. Optimize bitrate and resolution based on bandwidth.

What is Included in the Work

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.