A visitor approaches an exhibit and hears a narration without a single tap. Or scans a QR code on the plaque. Or manually enters the number. All three scenarios must work offline—museum Wi-Fi often fails. According to research, over 60% of visitors use mobile devices in museums, but an unstable network undermines the experience. That's why developing a mobile audio guide for a museum must account for autonomy, reliable detection, and resource efficiency. A well-designed architecture can reduce support and licensing costs—for instance, dropping commercial maps saves money. Our team builds audio guides for museums from scratch: from beacon selection to store publishing. Over the years, we've delivered dozens of projects, including large state museums. In this article, we'll dive into technical details: how we build a reliable offline system that never lets visitors down.
Contact us to discuss your project and get a preliminary estimate.
How Automatic Exhibit Detection Works
The most interesting scenario is a proximity trigger with no user action.
iBeacon / Bluetooth LE. Beacons near each exhibit (Estimote, Kontakt.io). iOS: CLLocationManager.startRangingBeacons(satisfying:) obtains a list of beacons with proximity (.immediate / .near / .far) and rssi. We pick the closest with .immediate and start audio. Important: we don't react to every proximity change—apply a 2-3 second debounce to avoid switching audio due to random signal fluctuations.
Android: BluetoothLeScanner with a filter by beacon manufacturer UUID. The AltBeacon library simplifies ranging. A Foreground Service is needed for background scanning—while the app is active, normal scanning suffices. Bluetooth LE — official Android documentation.
QR scanning. Fallback method. iOS: VNDetectBarcodesRequest (Vision) — faster than AVMetadataOutput for single scans. Android: ML Kit Barcode Scanning. Flutter: mobile_scanner.
Manual number entry. Always as a fallback—in case Bluetooth is off or beacons are missing.
Comparison of Detection Methods
| Method |
Accuracy |
Offline Capability |
Implementation Cost |
| iBeacon |
High (up to 1 m) |
Full (offline) |
Medium (beacons $10–30 each) |
| QR code |
High (camera-dependent) |
Full (offline) |
Low (paper labels) |
| Manual entry |
Low (input errors) |
Full (offline) |
Zero |
| NFC tags |
Medium (contact) |
Full (offline) |
Medium (tags $1–5 each) |
Offline Content and Data Structure
A museum with 200 exhibits means 200 audio files, images, and texts. Total 300–500 MB. Update strategy:
- On first launch, download a 'lightweight' package: texts, 200px previews, metadata (beacon UUIDs, exhibit numbers)
- Audio downloaded on demand or in batches with caching
Storage structure: CoreData/Room for exhibit metadata (id, title, beacon_uuid, qr_code, languages). Audio files in Application Support/filesDir with id→path mapping. SQLite query 'find exhibit by beacon UUID' is instantaneous.
Multilingualism: user selects language at start. Audio and texts stored with language suffix. Only the selected language is downloaded—saving space.
Audio caching details
For streaming playback, we use AVAssetResourceLoader (iOS) and CacheDataSource (ExoPlayer). Cache is stored up to 1 GB, evicted LRU when full. Only the current session's audio is guaranteed to be cached. Users can preload all audio via settings.
Exhibit Audio Player
AVPlayer (iOS) / ExoPlayer Media3 (Android) / just_audio (Flutter). Features specific to audio guides:
- Auto-stop when moving away from exhibit (proximity returns to far)
- Progress saved: returning to exhibit resumes from pause point
- Playback speed: 0.8x for elderly visitors, 1.5x for quick ones
- Lock screen controls via MPNowPlayingInfoCenter (iOS) / MediaSession (Android)
Interactive Exhibition Map
A hall map with exhibit markers. Not Google Maps—museums want no external dependencies or fees. We use an SVG hall map: rendered in a WKWebView with interactive SVG (iOS), WebView (Android), or flutter_svg with GestureDetector (Flutter).
Alternatively, a native implementation with Canvas/CustomPainter: load a PNG map as a base, draw exhibit points on top with pinch-to-zoom scaling.
Visitor's current location on the map is determined via the same iBeacon ranging: we identify the hall by the set of visible strong-signal beacons. No GPS—it doesn't work indoors. Dropping commercial maps saves up to several hundred dollars a year in licensing.
Why a CMS for the Museum Is Essential
Administrators update texts and audio without releasing a new app version. Backend: simple REST API + S3 for media files. On launch, the app checks the manifest version—if changed, it downloads the delta. This saves moderation time and allows quick updates to tour content.
How to Implement an Offline Audio Guide: Process
- Analysis and design. Gather requirements: number of exhibits, halls, languages, budget. Choose stack: native or cross-platform.
- Development. Backend (CMS, API), mobile app (player, map, detection), beacon integration.
- Testing. Real-world testing with beacons, offline mode, load testing.
- Deployment. Publish to App Store and Google Play, configure Code Signing, Provisioning Profile, TestFlight.
- Maintenance. 3-month warranty, then contract.
Timeline Comparison
| Phase |
Duration (basic version) |
Duration (extended version) |
| Analysis |
3-5 days |
5-7 days |
| Development |
2-3 weeks |
4-5 weeks |
| Testing |
1 week |
1.5 weeks |
| Deployment |
3-5 days |
5-7 days |
What's Included
- Architecture documentation and API description
- App source code (Git repository)
- Operating manual for staff
- Staff training (1-2 days)
- Test run and tuning with real beacons
- Support during warranty period
Timeline and Cost Estimation
Basic version (QR + offline + player + map) — 4-6 weeks. Version with iBeacon and CMS — 7-9 weeks. Exact cost is calculated after requirements analysis. We'll assess your project for free—contact us for a consultation.
Certified developers with 5+ years of experience guarantee quality. We've built 10+ audio guides for museums in Russia and the CIS. iBeacon is the standard for proximity solutions.
Get a free project estimate: contact us, and we'll prepare a commercial proposal.
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.
