Developing an NFT Collection Browser for Mobile Apps
The difference between this task and displaying collections in a wallet is fundamental: here the user browses other people's collections, explores the marketplace, filters by attributes and price. This is closer to e-commerce with a cryptocurrency twist—an endless feed of thousands of tokens, complex filters, and up-to-date prices. Our team has over 5 years of experience in mobile NFT development, completed 20+ projects with integration of OpenSea API and Reservoir API. We guarantee fast loading and smooth UX even with 10,000+ tokens.
How to Choose an API for an NFT Collection Browser?
The primary data source is OpenSea API v2. It's the richest source: collections, tokens, floor price, trade volumes, events (listings, sales). For public browsing without authentication—GET /api/v2/collection/{slug}/nfts with pagination by next cursor. Limit: 4 req/s on free plan.
Reservoir API aggregates data from OpenSea, Blur, LooksRare, X2Y2. For apps with trading features it's preferable: a single endpoint for floor price and best offer regardless of the marketplace.
Alchemy NFT API—getNFTsForCollection returns all tokens of a collection with metadata; getFloorPrice gives floor by marketplace.
For Solana collections: Magic Eden API (/collections/{symbol}/listings) or Tensor API.
| API |
Data |
Limits |
Trading Features |
| OpenSea v2 |
Tokens, collections, prices, events |
4 req/s (free) |
Yes (listings, sales) |
| Reservoir |
Aggregated from multiple exchanges |
2 req/s (free) |
Yes (best offer, floors) |
| Alchemy |
Tokens, metadata, floor |
Depends on plan |
Limited |
Reservoir returns a unified best offer 2–3 times faster compared to polling OpenSea and Blur separately. This saves 100–200 ms per token card load and reduces API request costs.
How to Implement Infinite Scroll and Pagination?
Collections like Bored Ape Yacht Club have 10,000 tokens. Loading everything at once is impossible. OpenSea returns cursor-based pagination (next in the response body). We implement via Paging 3 (Android) or a custom PaginatedViewModel on iOS with AsyncSequence.
On Flutter: infinite_scroll_pagination package. Load the next page when 80% of the list is reached, not at the very end—otherwise the user sees an empty space.
Caching scheme
Cache the first page in Room/CoreData—upon next open the screen appears instantly, updates happen in the background (stale-while-revalidate). This gives a feeling of speed 2x faster than without cache, and saves up to 40% of time on reloading.
Filtering and Sorting by Attributes
This is the most nontrivial part. NFT attributes have a dynamic schema: one collection has Background, Fur, Eyes; another has Rarity, Element, Power. The structure is unknown upfront.
Approach: on first load of the collection, request getContractMetadata or /collection/{slug}/attributes—get a list of trait types with values and rarity. Build the filter UI dynamically: ExpandableSection per trait type, Checkbox list for values.
Attribute filtering on the API side: OpenSea accepts trait_type/value as query params. Reservoir uses a JSON body with an array of filters. Local filtering only works if the entire collection is cached—for 10K tokens this is unrealistic.
Sorting: by price (asc/desc), by rarity (needs a rarity score—either compute from attributes or fetch from rarity.tools API). By token number.
Detailed Token Card
Fast loading is critical. Image—immediately in maximum resolution, not after tap. Card structure:
- Media: image, animated GIF, video (AVPlayer / ExoPlayer), 3D (SceneKit / SceneView Flutter)
- Traits as chips with rarity percentage
- Current listing and best offer
- Price history (sparkline graph via Charts framework / MPAndroidChart)
- "Buy" button—if marketplace SDK is integrated
Transition to card—shared element transition: image from grid smoothly enlarges. iOS: UIViewControllerTransitioningDelegate + UIPresentationController. Android: sharedElementEnterTransition with Compose sharedBounds.
What's Included in the Deliverable?
We provide a full package: API integration documentation, commented source code, configured authorization schemes (OAuth/API keys), deployment instructions for App Store and Google Play, and 30-day post-delivery support. We guarantee compliance with App Store Review Guidelines (Sections 4.2, 5.1) and Google Play policies.
Pagination Library Comparison by Platform
| Platform |
Library |
Features |
| Android |
Paging 3 |
Built-in Room support, RxJava, LiveData, coroutines |
| iOS |
PaginatedViewModel (AsyncSequence) |
Custom but flexible; easy to customize preloading |
| Flutter |
infinite_scroll_pagination |
Simple setup, ScrollController support |
The cost of developing an NFT collection browser depends on integration complexity and chosen API. Budget savings are achieved by using ready-made components and caching. Assess your project—contact us for a free requirements analysis. We'll select the optimal solution and suggest timelines. Order a turnkey NFT browser development and get a finished product with marketplace integration in 3–5 days.
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.
