# Mobile Optimization Guide for AR/USDZ

## Overview

This guide focuses on optimizing USDZ content and AR experiences specifically for mobile devices. Learn about performance constraints, battery optimization, thermal management, and device-specific considerations for creating smooth AR experiences.

## Mobile Hardware Constraints

### GPU Performance Tiers

```python
class MobilePerformanceTier:
    """Classify mobile devices by AR performance capability"""
    
    PERFORMANCE_TIERS = {
        'high_end': {
            'max_vertices': 50000,
            'max_texture_size': 2048,
            'max_materials': 10,
            'supports_pbr': True,
            'supports_shadows': True,
            'target_fps': 60,
            'examples': ['iPhone 14 Pro', 'iPhone 13 Pro', 'iPad Pro M1/M2', 'Samsung Galaxy S22+']
        },
        'mid_range': {
            'max_vertices': 25000,
            'max_texture_size': 1024,
            'max_materials': 5,
            'supports_pbr': True,
            'supports_shadows': False,
            'target_fps': 30,
            'examples': ['iPhone 12', 'iPhone 11', 'iPad Air', 'Samsung Galaxy A series']
        },
        'low_end': {
            'max_vertices': 10000,
            'max_texture_size': 512,
            'max_materials': 3,
            'supports_pbr': False,
            'supports_shadows': False,
            'target_fps': 30,
            'examples': ['iPhone SE', 'older Android devices']
        }
    }
    
    @classmethod
    def detect_device_tier(cls, device_info=None):
        """Detect device performance tier"""
        
        # In practice, this would use device detection APIs
        # For now, return conservative estimate
        return 'mid_range'
    
    @classmethod
    def get_optimization_params(cls, tier):
        """Get optimization parameters for device tier"""
        return cls.PERFORMANCE_TIERS.get(tier, cls.PERFORMANCE_TIERS['low_end'])

# Device-specific optimization
def optimize_for_device_tier(usd_stage, tier='mid_range'):
    """Optimize USD stage for specific device tier"""
    
    params = MobilePerformanceTier.get_optimization_params(tier)
    
    # Apply geometry optimization
    optimize_geometry_for_mobile(usd_stage, params['max_vertices'])
    
    # Apply texture optimization  
    optimize_textures_for_mobile(usd_stage, params['max_texture_size'])
    
    # Simplify materials if needed
    if not params['supports_pbr']:
        simplify_materials_for_mobile(usd_stage)
    
    return usd_stage
```

### Memory Management

```python
class MobileMemoryOptimizer:
    """Optimize memory usage for mobile AR applications"""
    
    def __init__(self, target_memory_mb=150):
        self.target_memory = target_memory_mb * 1024 * 1024  # Convert to bytes
        self.current_usage = 0
    
    def estimate_texture_memory(self, width, height, format='RGBA'):
        """Estimate texture memory usage"""
        
        bytes_per_pixel = {
            'RGBA': 4,
            'RGB': 3,
            'GRAY': 1,
            'RGBA_COMPRESSED': 1,  # Approximate for compressed formats
        }
        
        return width * height * bytes_per_pixel.get(format, 4)
    
    def estimate_geometry_memory(self, vertex_count, has_normals=True, has_uvs=True):
        """Estimate geometry memory usage"""
        
        # Position (3 floats) + Normal (3 floats) + UV (2 floats) = 8 floats per vertex
        components_per_vertex = 3  # Position
        if has_normals:
            components_per_vertex += 3
        if has_uvs:
            components_per_vertex += 2
        
        return vertex_count * components_per_vertex * 4  # 4 bytes per float
    
    def optimize_texture_memory(self, textures_info):
        """Optimize texture memory usage"""
        
        total_texture_memory = 0
        optimized_textures = []
        
        for texture in textures_info:
            estimated_memory = self.estimate_texture_memory(
                texture['width'], texture['height'], texture['format']
            )
            
            if total_texture_memory + estimated_memory > self.target_memory * 0.7:  # 70% for textures
                # Reduce texture size
                new_size = self._calculate_reduced_size(texture['width'], texture['height'])
                texture['width'], texture['height'] = new_size
                estimated_memory = self.estimate_texture_memory(new_size[0], new_size[1], texture['format'])
            
            total_texture_memory += estimated_memory
            optimized_textures.append(texture)
        
        return optimized_textures
    
    def _calculate_reduced_size(self, width, height):
        """Calculate reduced texture size while maintaining aspect ratio"""
        
        # Reduce by half while keeping power-of-2
        new_width = max(256, width // 2)
        new_height = max(256, height // 2)
        
        # Ensure power of 2
        new_width = 2 ** (new_width - 1).bit_length() // 2
        new_height = 2 ** (new_height - 1).bit_length() // 2
        
        return new_width, new_height

# Geometry optimization for mobile
def optimize_geometry_for_mobile(usd_stage, max_vertices=25000):
    """Optimize geometry for mobile performance"""
    
    from pxr import UsdGeom
    
    for prim in usd_stage.Traverse():
        if prim.IsA(UsdGeom.Mesh):
            mesh = UsdGeom.Mesh(prim)
            
            # Get current vertex count
            points_attr = mesh.GetPointsAttr()
            if points_attr:
                points = points_attr.Get()
                current_vertex_count = len(points) if points else 0
                
                if current_vertex_count > max_vertices:
                    # Apply mesh decimation
                    apply_mesh_decimation(mesh, max_vertices)
    
    return usd_stage

def apply_mesh_decimation(mesh, target_vertices):
    """Apply mesh decimation to reduce vertex count"""
    
    # This is a simplified example - real implementation would use
    # libraries like Open3D or pymeshlab for mesh decimation
    
    print(f"Decimating mesh to {target_vertices} vertices")
    
    # In practice, you would:
    # 1. Extract mesh data from USD
    # 2. Apply decimation algorithm
    # 3. Update USD mesh with simplified geometry
    
    return mesh
```

## Texture Optimization Strategies

### Compression and Format Selection

```python
class MobileTextureOptimizer:
    """Optimize textures for mobile AR performance"""
    
    def __init__(self):
        self.supported_formats = {
            'ios': ['PNG', 'JPEG', 'HEIF', 'AVIF'],
            'android': ['PNG', 'JPEG', 'WebP', 'ASTC'],
            'web': ['PNG', 'JPEG', 'WebP']
        }
    
    def optimize_texture_for_platform(self, image_path, platform='ios', quality='medium'):
        """Optimize texture for specific platform"""
        
        from PIL import Image
        import os
        
        with Image.open(image_path) as img:
            # Determine optimal format
            optimal_format = self._select_optimal_format(img, platform)
            
            # Resize for quality level
            optimized_img = self._resize_for_quality(img, quality)
            
            # Apply compression
            output_path = self._apply_compression(optimized_img, optimal_format, quality)
            
            return output_path
    
    def _select_optimal_format(self, image, platform):
        """Select optimal image format for platform"""
        
        has_transparency = image.mode in ('RGBA', 'LA') or 'transparency' in image.info
        
        if platform == 'ios':
            return 'PNG' if has_transparency else 'JPEG'
        elif platform == 'android':
            return 'PNG' if has_transparency else 'WebP'
        else:  # web
            return 'PNG' if has_transparency else 'JPEG'
    
    def _resize_for_quality(self, image, quality):
        """Resize image based on quality setting"""
        
        width, height = image.size
        
        quality_settings = {
            'low': 512,
            'medium': 1024,
            'high': 2048
        }
        
        max_size = quality_settings.get(quality, 1024)
        
        if max(width, height) > max_size:
            ratio = max_size / max(width, height)
            new_width = int(width * ratio)
            new_height = int(height * ratio)
            
            # Ensure power of 2
            new_width = 2 ** (new_width - 1).bit_length() if new_width > 0 else 1
            new_height = 2 ** (new_height - 1).bit_length() if new_height > 0 else 1
            
            return image.resize((new_width, new_height), Image.Resampling.LANCZOS)
        
        return image
    
    def _apply_compression(self, image, format_name, quality):
        """Apply format-specific compression"""
        
        output_path = f"optimized.{format_name.lower()}"
        
        if format_name == 'JPEG':
            quality_value = {'low': 60, 'medium': 80, 'high': 90}[quality]
            image.save(output_path, 'JPEG', quality=quality_value, optimize=True)
        
        elif format_name == 'PNG':
            image.save(output_path, 'PNG', optimize=True, compress_level=9)
        
        elif format_name == 'WebP':
            quality_value = {'low': 60, 'medium': 80, 'high': 90}[quality]
            image.save(output_path, 'WebP', quality=quality_value, method=6)
        
        return output_path
    
    def create_texture_atlas(self, texture_paths, atlas_size=1024):
        """Combine multiple textures into single atlas for better performance"""
        
        from PIL import Image
        import math
        
        # Load all textures
        textures = [Image.open(path) for path in texture_paths]
        
        # Calculate grid layout
        texture_count = len(textures)
        grid_size = math.ceil(math.sqrt(texture_count))
        cell_size = atlas_size // grid_size
        
        # Create atlas
        atlas = Image.new('RGBA', (atlas_size, atlas_size), (0, 0, 0, 0))
        
        uv_mapping = {}
        
        for i, texture in enumerate(textures):
            row = i // grid_size
            col = i % grid_size
            
            # Resize texture to fit cell
            resized_texture = texture.resize((cell_size, cell_size), Image.Resampling.LANCZOS)
            
            # Paste into atlas
            x = col * cell_size
            y = row * cell_size
            atlas.paste(resized_texture, (x, y))
            
            # Calculate UV coordinates
            uv_mapping[texture_paths[i]] = {
                'min_u': x / atlas_size,
                'min_v': y / atlas_size,
                'max_u': (x + cell_size) / atlas_size,
                'max_v': (y + cell_size) / atlas_size
            }
        
        return atlas, uv_mapping

# Automatic LOD generation
def generate_texture_lods(texture_path, levels=3):
    """Generate multiple levels of detail for textures"""
    
    from PIL import Image
    
    with Image.open(texture_path) as img:
        base_width, base_height = img.size
        lod_textures = []
        
        for level in range(levels):
            # Each LOD is half the size of the previous
            scale_factor = 0.5 ** level
            new_width = max(64, int(base_width * scale_factor))
            new_height = max(64, int(base_height * scale_factor))
            
            # Ensure power of 2
            new_width = 2 ** (new_width - 1).bit_length() if new_width > 0 else 64
            new_height = 2 ** (new_height - 1).bit_length() if new_height > 0 else 64
            
            lod_img = img.resize((new_width, new_height), Image.Resampling.LANCZOS)
            
            lod_path = texture_path.replace('.', f'_lod{level}.')
            lod_img.save(lod_path)
            lod_textures.append(lod_path)
        
        return lod_textures
```

## Battery and Thermal Optimization

### Frame Rate Management

```python
class MobilePerformanceManager:
    """Manage performance for optimal battery life and thermal behavior"""
    
    def __init__(self):
        self.target_fps = 30  # Conservative for mobile
        self.thermal_state = 'normal'
        self.battery_level = 1.0
        self.performance_mode = 'balanced'
    
    def update_performance_settings(self, thermal_state, battery_level):
        """Update performance based on device state"""
        
        self.thermal_state = thermal_state
        self.battery_level = battery_level
        
        # Adjust performance mode
        if thermal_state == 'critical' or battery_level < 0.2:
            self.performance_mode = 'power_save'
            self.target_fps = 20
        elif thermal_state == 'serious' or battery_level < 0.5:
            self.performance_mode = 'balanced'
            self.target_fps = 30
        else:
            self.performance_mode = 'performance'
            self.target_fps = 60
    
    def get_rendering_settings(self):
        """Get current rendering settings based on performance mode"""
        
        settings = {
            'power_save': {
                'shadow_quality': 'off',
                'texture_quality': 'low',
                'geometry_quality': 'low',
                'post_processing': 'off',
                'particle_count': 'minimal'
            },
            'balanced': {
                'shadow_quality': 'low',
                'texture_quality': 'medium',
                'geometry_quality': 'medium',
                'post_processing': 'basic',
                'particle_count': 'reduced'
            },
            'performance': {
                'shadow_quality': 'medium',
                'texture_quality': 'high',
                'geometry_quality': 'high',
                'post_processing': 'full',
                'particle_count': 'normal'
            }
        }
        
        return settings.get(self.performance_mode, settings['balanced'])
    
    def should_reduce_quality(self, current_fps):
        """Determine if quality should be reduced based on performance"""
        
        fps_threshold = self.target_fps * 0.8  # 80% of target
        
        if current_fps < fps_threshold:
            return True
        
        # Also reduce quality if thermal throttling
        if self.thermal_state in ['serious', 'critical']:
            return True
        
        return False

# Adaptive quality system
class AdaptiveQualitySystem:
    """Automatically adjust quality based on real-time performance"""
    
    def __init__(self):
        self.performance_history = []
        self.quality_level = 'medium'
        self.adjustment_cooldown = 0
    
    def update_frame_stats(self, frame_time_ms, gpu_time_ms=None):
        """Update frame statistics"""
        
        fps = 1000.0 / frame_time_ms if frame_time_ms > 0 else 0
        
        self.performance_history.append({
            'fps': fps,
            'frame_time': frame_time_ms,
            'gpu_time': gpu_time_ms,
            'timestamp': time.time()
        })
        
        # Keep only recent history (last 60 frames)
        if len(self.performance_history) > 60:
            self.performance_history.pop(0)
        
        # Check if adjustment is needed
        if self.adjustment_cooldown <= 0:
            self._evaluate_quality_adjustment()
            self.adjustment_cooldown = 30  # Wait 30 frames before next adjustment
        else:
            self.adjustment_cooldown -= 1
    
    def _evaluate_quality_adjustment(self):
        """Evaluate if quality adjustment is needed"""
        
        if len(self.performance_history) < 30:
            return
        
        # Calculate average FPS over recent frames
        recent_fps = [frame['fps'] for frame in self.performance_history[-30:]]
        avg_fps = sum(recent_fps) / len(recent_fps)
        
        target_fps = 30
        
        if avg_fps < target_fps * 0.8:  # Below 80% of target
            self._reduce_quality()
        elif avg_fps > target_fps * 1.1 and self.quality_level != 'high':  # Above 110% of target
            self._increase_quality()
    
    def _reduce_quality(self):
        """Reduce rendering quality"""
        
        quality_levels = ['high', 'medium', 'low']
        current_index = quality_levels.index(self.quality_level)
        
        if current_index < len(quality_levels) - 1:
            self.quality_level = quality_levels[current_index + 1]
            print(f"Quality reduced to: {self.quality_level}")
    
    def _increase_quality(self):
        """Increase rendering quality"""
        
        quality_levels = ['low', 'medium', 'high']
        current_index = quality_levels.index(self.quality_level)
        
        if current_index < len(quality_levels) - 1:
            self.quality_level = quality_levels[current_index + 1]
            print(f"Quality increased to: {self.quality_level}")
```

## Network and Loading Optimization

### Progressive Loading

```python
class ProgressiveLoader:
    """Implement progressive loading for mobile AR content"""
    
    def __init__(self):
        self.loading_stages = [
            {'name': 'base_geometry', 'priority': 1, 'size_mb': 2},
            {'name': 'base_textures', 'priority': 2, 'size_mb': 5},
            {'name': 'detailed_textures', 'priority': 3, 'size_mb': 10},
            {'name': 'additional_details', 'priority': 4, 'size_mb': 8}
        ]
    
    def create_progressive_usdz(self, assets, output_dir):
        """Create USDZ files for progressive loading"""
        
        base_package = self._create_base_package(assets, output_dir)
        enhancement_packages = self._create_enhancement_packages(assets, output_dir)
        
        return {
            'base': base_package,
            'enhancements': enhancement_packages
        }
    
    def _create_base_package(self, assets, output_dir):
        """Create minimal base package for immediate viewing"""
        
        # Use lowest quality textures and simplified geometry
        base_assets = {
            'geometry': self._simplify_geometry(assets['geometry'], target_vertices=5000),
            'textures': self._reduce_texture_quality(assets['textures'], quality='low')
        }
        
        base_path = output_dir / 'base.usdz'
        self._package_assets(base_assets, base_path)
        
        return base_path
    
    def _create_enhancement_packages(self, assets, output_dir):
        """Create enhancement packages for progressive quality improvement"""
        
        enhancements = []
        
        # Medium quality package
        medium_assets = {
            'textures': self._reduce_texture_quality(assets['textures'], quality='medium')
        }
        medium_path = output_dir / 'medium_textures.usdz'
        self._package_assets(medium_assets, medium_path)
        enhancements.append(medium_path)
        
        # High quality package
        high_assets = {
            'textures': assets['textures'],  # Original quality
            'additional_geometry': assets.get('detail_geometry', [])
        }
        high_path = output_dir / 'high_quality.usdz'
        self._package_assets(high_assets, high_path)
        enhancements.append(high_path)
        
        return enhancements
    
    def generate_loading_manifest(self, packages, connection_speed='medium'):
        """Generate loading manifest based on connection speed"""
        
        manifests = {
            'slow': {
                'initial': ['base'],
                'progressive': ['medium_textures'],
                'optional': ['high_quality']
            },
            'medium': {
                'initial': ['base', 'medium_textures'],
                'progressive': ['high_quality'],
                'optional': []
            },
            'fast': {
                'initial': ['base', 'medium_textures', 'high_quality'],
                'progressive': [],
                'optional': []
            }
        }
        
        return manifests.get(connection_speed, manifests['medium'])

# Network-aware loading
class NetworkAwareLoader:
    """Load content based on network conditions"""
    
    def __init__(self):
        self.connection_monitor = NetworkConnectionMonitor()
    
    def select_optimal_quality(self, available_qualities):
        """Select optimal quality based on network conditions"""
        
        connection_info = self.connection_monitor.get_connection_info()
        
        if connection_info['effective_type'] in ['slow-2g', '2g']:
            return 'low'
        elif connection_info['effective_type'] == '3g':
            return 'medium'
        else:
            return 'high'
    
    def estimate_download_time(self, file_size_mb, connection_speed):
        """Estimate download time based on file size and connection"""
        
        # Speed estimates in Mbps
        speeds = {
            'slow-2g': 0.25,
            '2g': 0.5,
            '3g': 2.0,
            '4g': 10.0,
            'wifi': 50.0
        }
        
        speed_mbps = speeds.get(connection_speed, 2.0)
        download_time_seconds = (file_size_mb * 8) / speed_mbps  # Convert MB to Mb, divide by Mbps
        
        return download_time_seconds
    
    def should_preload(self, file_size_mb, priority, battery_level):
        """Determine if file should be preloaded"""
        
        # Don't preload large files on low battery
        if battery_level < 0.3 and file_size_mb > 5:
            return False
        
        # Only preload high priority items on slow connections
        if self.connection_monitor.is_slow_connection() and priority > 2:
            return False
        
        return True

class NetworkConnectionMonitor:
    """Monitor network connection status"""
    
    def get_connection_info(self):
        """Get current connection information"""
        
        # In a real implementation, this would use platform-specific APIs
        # For web: navigator.connection
        # For iOS: Network framework
        # For Android: ConnectivityManager
        
        return {
            'effective_type': '4g',  # Mock data
            'downlink': 10.0,        # Mbps
            'rtt': 100,              # ms
            'save_data': False
        }
    
    def is_slow_connection(self):
        """Check if connection is considered slow"""
        
        info = self.get_connection_info()
        return info['effective_type'] in ['slow-2g', '2g', '3g']
    
    def is_metered_connection(self):
        """Check if connection is metered (cellular)"""
        
        info = self.get_connection_info()
        return info.get('save_data', False) or info['effective_type'] != 'wifi'
```

## Platform-Specific Optimizations

### iOS Optimizations

```python
class iOSOptimizer:
    """iOS-specific optimizations for AR content"""
    
    def __init__(self):
        self.device_capabilities = self._detect_ios_capabilities()
    
    def _detect_ios_capabilities(self):
        """Detect iOS device capabilities"""
        
        # In practice, this would use iOS device detection
        return {
            'has_lidar': False,      # iPhone 12 Pro and later
            'neural_engine': True,   # A12 and later
            'metal_performance': 'high',
            'memory_gb': 4,
            'thermal_design': 'compact'
        }
    
    def optimize_for_ios_device(self, usd_stage):
        """Apply iOS-specific optimizations"""
        
        # Use Metal-optimized formats
        self._optimize_for_metal_gpu(usd_stage)
        
        # Optimize for iOS memory management
        self._apply_ios_memory_optimizations(usd_stage)
        
        # Use iOS-specific features if available
        if self.device_capabilities['has_lidar']:
            self._enable_lidar_optimizations(usd_stage)
        
        return usd_stage
    
    def _optimize_for_metal_gpu(self, usd_stage):
        """Optimize for Metal GPU performance"""
        
        # Metal prefers certain texture formats and layouts
        # Optimize vertex buffer layouts for Metal
        # Use Metal-specific compression formats where possible
        
        print("Applying Metal GPU optimizations")
    
    def _apply_ios_memory_optimizations(self, usd_stage):
        """Apply iOS memory management optimizations"""
        
        # iOS has aggressive memory management
        # Optimize for smaller memory footprint
        # Use texture streaming where possible
        
        print("Applying iOS memory optimizations")
    
    def _enable_lidar_optimizations(self, usd_stage):
        """Enable LiDAR-specific optimizations"""
        
        # Add metadata for LiDAR-enhanced occlusion
        # Optimize geometry for mesh reconstruction
        # Enable advanced AR features
        
        print("Enabling LiDAR optimizations")
    
    def create_ios_ar_metadata(self):
        """Create iOS AR-specific metadata"""
        
        return {
            'arQuickLookCompatible': True,
            'behaviorConfiguration': 'automatic',
            'planeDetection': ['horizontal'],
            'environmentTexturing': 'automatic',
            'peopleOcclusion': self.device_capabilities['has_lidar'],
            'objectOcclusion': self.device_capabilities['has_lidar']
        }

# iOS-specific material optimizations
def optimize_materials_for_ios(usd_stage):
    """Optimize materials specifically for iOS Metal renderer"""
    
    from pxr import UsdShade
    
    for prim in usd_stage.Traverse():
        if prim.IsA(UsdShade.Material):
            material = UsdShade.Material(prim)
            
            # Get surface shader
            surface_output = material.GetSurfaceOutput()
            if surface_output.HasConnectedSource():
                source_info = surface_output.GetConnectedSource()[0]
                shader = UsdShade.Shader(source_info.GetPrim())
                
                # iOS/Metal specific optimizations
                _optimize_shader_for_metal(shader)
    
    return usd_stage

def _optimize_shader_for_metal(shader):
    """Optimize individual shader for Metal renderer"""
    
    # Disable features that are expensive on mobile Metal
    if shader.GetInput('clearcoat'):
        # Clearcoat is expensive on mobile
        shader.GetInput('clearcoat').Set(0.0)
    
    # Optimize roughness values for mobile
    roughness_input = shader.GetInput('roughness')
    if roughness_input and not roughness_input.HasConnectedSource():
        roughness_value = roughness_input.Get()
        if roughness_value is not None and roughness_value < 0.1:
            # Very low roughness is expensive on mobile
            roughness_input.Set(0.2)
```

### Android Optimizations

```python
class AndroidOptimizer:
    """Android-specific optimizations for AR content"""
    
    def __init__(self):
        self.arcore_capabilities = self._detect_arcore_capabilities()
    
    def _detect_arcore_capabilities(self):
        """Detect ARCore capabilities"""
        
        return {
            'supports_depth': False,     # Depth API
            'supports_instant_placement': True,
            'supports_light_estimation': True,
            'gpu_vendor': 'qualcomm',    # or 'mali', 'powervr'
            'vulkan_support': True
        }
    
    def convert_usdz_to_android_format(self, usdz_path, output_path):
        """Convert USDZ to Android-compatible format"""
        
        # Extract USDZ and convert to glTF
        gltf_path = self._convert_to_gltf(usdz_path)
        
        # Optimize for Android ARCore
        optimized_gltf = self._optimize_gltf_for_arcore(gltf_path)
        
        # Apply Android-specific optimizations
        final_path = self._apply_android_optimizations(optimized_gltf, output_path)
        
        return final_path
    
    def _optimize_gltf_for_arcore(self, gltf_path):
        """Optimize glTF for ARCore performance"""
        
        # Use Draco compression for geometry
        # Optimize texture formats for Android GPUs
        # Apply ARCore-specific material optimizations
        
        print(f"Optimizing {gltf_path} for ARCore")
        
        # In practice, would use gltf-pipeline or similar tools
        return gltf_path
    
    def _apply_android_optimizations(self, input_path, output_path):
        """Apply Android-specific optimizations"""
        
        gpu_vendor = self.arcore_capabilities['gpu_vendor']
        
        if gpu_vendor == 'qualcomm':
            self._optimize_for_adreno_gpu(input_path, output_path)
        elif gpu_vendor == 'mali':
            self._optimize_for_mali_gpu(input_path, output_path)
        else:
            # Generic optimization
            self._apply_generic_mobile_optimization(input_path, output_path)
        
        return output_path
    
    def _optimize_for_adreno_gpu(self, input_path, output_path):
        """Optimize for Qualcomm Adreno GPU"""
        
        # Adreno-specific optimizations:
        # - Prefer certain texture compression formats
        # - Optimize vertex shader complexity
        # - Use Adreno-specific extensions where available
        
        print("Applying Adreno GPU optimizations")
    
    def _optimize_for_mali_gpu(self, input_path, output_path):
        """Optimize for ARM Mali GPU"""
        
        # Mali-specific optimizations:
        # - Optimize for tile-based rendering
        # - Prefer specific texture formats
        # - Minimize bandwidth usage
        
        print("Applying Mali GPU optimizations")
    
    def create_scene_viewer_metadata(self):
        """Create metadata for Google Scene Viewer"""
        
        return {
            'model-viewer': {
                'ar': True,
                'ar-modes': 'scene-viewer',
                'ar-scale': 'auto',
                'auto-rotate': True,
                'camera-controls': True
            }
        }
```

## Performance Monitoring and Analytics

### Real-Time Performance Monitoring

```python
class MobilePerformanceMonitor:
    """Monitor AR performance on mobile devices"""
    
    def __init__(self):
        self.frame_times = []
        self.gpu_times = []
        self.memory_usage = []
        self.thermal_states = []
        self.battery_levels = []
        
        self.performance_targets = {
            'fps': 30,
            'frame_time_ms': 33.33,  # 1000/30
            'gpu_time_ms': 25,
            'memory_mb': 150,
            'temperature_c': 45
        }
    
    def record_frame_stats(self, frame_time_ms, gpu_time_ms=None, memory_mb=None):
        """Record frame performance statistics"""
        
        timestamp = time.time()
        
        self.frame_times.append({
            'timestamp': timestamp,
            'frame_time_ms': frame_time_ms,
            'fps': 1000.0 / frame_time_ms if frame_time_ms > 0 else 0
        })
        
        if gpu_time_ms is not None:
            self.gpu_times.append({
                'timestamp': timestamp,
                'gpu_time_ms': gpu_time_ms
            })
        
        if memory_mb is not None:
            self.memory_usage.append({
                'timestamp': timestamp,
                'memory_mb': memory_mb
            })
        
        # Keep only recent data (last 300 frames ~ 10 seconds at 30fps)
        self._trim_history(300)
    
    def record_thermal_state(self, temperature_c, thermal_state):
        """Record thermal performance data"""
        
        self.thermal_states.append({
            'timestamp': time.time(),
            'temperature_c': temperature_c,
            'thermal_state': thermal_state  # 'normal', 'warning', 'critical'
        })
    
    def record_battery_level(self, battery_percentage):
        """Record battery level"""
        
        self.battery_levels.append({
            'timestamp': time.time(),
            'battery_percentage': battery_percentage
        })
    
    def get_performance_summary(self, duration_seconds=10):
        """Get performance summary for recent duration"""
        
        cutoff_time = time.time() - duration_seconds
        
        # Filter recent frame times
        recent_frames = [f for f in self.frame_times if f['timestamp'] >= cutoff_time]
        
        if not recent_frames:
            return None
        
        # Calculate statistics
        frame_times = [f['frame_time_ms'] for f in recent_frames]
        fps_values = [f['fps'] for f in recent_frames]
        
        summary = {
            'avg_fps': sum(fps_values) / len(fps_values),
            'min_fps': min(fps_values),
            'max_fps': max(fps_values),
            'avg_frame_time_ms': sum(frame_times) / len(frame_times),
            'frame_drops': len([f for f in frame_times if f > self.performance_targets['frame_time_ms'] * 1.5]),
            'total_frames': len(recent_frames),
            'performance_score': self._calculate_performance_score(recent_frames)
        }
        
        return summary
    
    def _calculate_performance_score(self, frame_data):
        """Calculate overall performance score (0-100)"""
        
        if not frame_data:
            return 0
        
        avg_fps = sum(f['fps'] for f in frame_data) / len(frame_data)
        target_fps = self.performance_targets['fps']
        
        # Base score on FPS performance
        fps_score = min(100, (avg_fps / target_fps) * 100)
        
        # Penalty for frame drops
        frame_drops = len([f for f in frame_data if f['frame_time_ms'] > target_fps * 1.5])
        drop_penalty = (frame_drops / len(frame_data)) * 30
        
        final_score = max(0, fps_score - drop_penalty)
        
        return final_score
    
    def _trim_history(self, max_entries):
        """Trim history to keep only recent entries"""
        
        if len(self.frame_times) > max_entries:
            self.frame_times = self.frame_times[-max_entries:]
        
        if len(self.gpu_times) > max_entries:
            self.gpu_times = self.gpu_times[-max_entries:]
        
        if len(self.memory_usage) > max_entries:
            self.memory_usage = self.memory_usage[-max_entries:]
    
    def generate_performance_report(self):
        """Generate comprehensive performance report"""
        
        summary = self.get_performance_summary()
        
        if not summary:
            return "No performance data available"
        
        report = f"""
Mobile AR Performance Report
============================

Frame Rate Performance:
- Average FPS: {summary['avg_fps']:.1f}
- Min FPS: {summary['min_fps']:.1f}
- Max FPS: {summary['max_fps']:.1f}
- Frame Drops: {summary['frame_drops']} / {summary['total_frames']}

Overall Performance Score: {summary['performance_score']:.1f}/100

Recommendations:
{self._generate_recommendations(summary)}
"""
        
        return report
    
    def _generate_recommendations(self, summary):
        """Generate performance improvement recommendations"""
        
        recommendations = []
        
        if summary['avg_fps'] < 25:
            recommendations.append("- Reduce geometry complexity or texture resolution")
        
        if summary['frame_drops'] > summary['total_frames'] * 0.1:
            recommendations.append("- Implement adaptive quality to reduce frame drops")
        
        if summary['performance_score'] < 70:
            recommendations.append("- Consider platform-specific optimizations")
        
        if not recommendations:
            recommendations.append("- Performance is good, consider enabling higher quality features")
        
        return "\n".join(recommendations)

# Mobile-specific analytics
class MobileARAnalytics:
    """Analytics specifically for mobile AR performance"""
    
    def __init__(self, analytics_endpoint):
        self.endpoint = analytics_endpoint
        self.device_info = self._gather_device_info()
    
    def _gather_device_info(self):
        """Gather device information for analytics"""
        
        # In practice, would use platform-specific APIs
        return {
            'platform': 'ios',  # or 'android'
            'device_model': 'iPhone 14',
            'os_version': '16.0',
            'gpu_model': 'Apple A16',
            'memory_gb': 6,
            'ar_support': 'arkit'
        }
    
    def track_ar_session(self, session_data):
        """Track AR session with mobile-specific metrics"""
        
        mobile_metrics = {
            'device_info': self.device_info,
            'session_duration_ms': session_data['duration'],
            'avg_fps': session_data['avg_fps'],
            'frame_drops': session_data['frame_drops'],
            'thermal_events': session_data.get('thermal_events', 0),
            'battery_drain_percent': session_data.get('battery_drain', 0),
            'model_complexity': session_data.get('vertex_count', 0),
            'texture_memory_mb': session_data.get('texture_memory', 0)
        }
        
        self._send_analytics('mobile_ar_session', mobile_metrics)
    
    def track_performance_issue(self, issue_type, details):
        """Track performance issues for debugging"""
        
        issue_data = {
            'device_info': self.device_info,
            'issue_type': issue_type,
            'details': details,
            'timestamp': time.time()
        }
        
        self._send_analytics('performance_issue', issue_data)
    
    def _send_analytics(self, event_type, data):
        """Send analytics data to endpoint"""
        
        payload = {
            'event_type': event_type,
            'data': data,
            'timestamp': time.time()
        }
        
        # In practice, would send to analytics service
        print(f"Analytics: {event_type} - {data}")
```

## Testing and Validation

### Mobile AR Testing Framework

```python
class MobileARTestSuite:
    """Comprehensive testing suite for mobile AR content"""
    
    def __init__(self):
        self.test_devices = [
            {'platform': 'ios', 'model': 'iPhone 12', 'performance_tier': 'mid_range'},
            {'platform': 'ios', 'model': 'iPhone 14 Pro', 'performance_tier': 'high_end'},
            {'platform': 'android', 'model': 'Pixel 6', 'performance_tier': 'mid_range'},
            {'platform': 'android', 'model': 'Galaxy S22', 'performance_tier': 'high_end'},
        ]
    
    def run_comprehensive_tests(self, content_package):
        """Run comprehensive mobile AR tests"""
        
        test_results = {}
        
        for device in self.test_devices:
            device_results = self._test_on_device(content_package, device)
            test_results[f"{device['platform']}_{device['model']}"] = device_results
        
        return self._generate_test_report(test_results)
    
    def _test_on_device(self, content_package, device):
        """Test content on specific device"""
        
        results = {
            'device': device,
            'load_time_test': self._test_load_time(content_package, device),
            'performance_test': self._test_performance(content_package, device),
            'memory_test': self._test_memory_usage(content_package, device),
            'battery_test': self._test_battery_impact(content_package, device),
            'thermal_test': self._test_thermal_impact(content_package, device),
            'quality_test': self._test_visual_quality(content_package, device)
        }
        
        return results
    
    def _test_load_time(self, content_package, device):
        """Test content loading time"""
        
        # Simulate loading based on device performance
        base_load_time = 5.0  # seconds
        
        # Adjust based on device tier
        if device['performance_tier'] == 'high_end':
            load_time = base_load_time * 0.6
        elif device['performance_tier'] == 'mid_range':
            load_time = base_load_time * 1.0
        else:  # low_end
            load_time = base_load_time * 1.8
        
        # File size impact
        file_size_mb = self._get_file_size(content_package)
        load_time += (file_size_mb - 10) * 0.2  # Additional time per MB over 10MB
        
        return {
            'load_time_seconds': max(1.0, load_time),
            'acceptable': load_time < 10.0,
            'target_time': 5.0
        }
    
    def _test_performance(self, content_package, device):
        """Test runtime performance"""
        
        # Estimate performance based on content complexity and device
        vertex_count = self._estimate_vertex_count(content_package)
        texture_memory = self._estimate_texture_memory(content_package)
        
        performance_score = self._calculate_performance_score(
            vertex_count, texture_memory, device
        )
        
        return {
            'performance_score': performance_score,
            'estimated_fps': max(15, 60 - (100 - performance_score) * 0.5),
            'acceptable': performance_score >= 60
        }
    
    def _generate_test_report(self, test_results):
        """Generate comprehensive test report"""
        
        report = {
            'overall_compatibility': self._calculate_overall_compatibility(test_results),
            'device_results': test_results,
            'recommendations': self._generate_recommendations(test_results),
            'critical_issues': self._identify_critical_issues(test_results)
        }
        
        return report
    
    def _calculate_overall_compatibility(self, test_results):
        """Calculate overall compatibility score"""
        
        total_score = 0
        total_tests = 0
        
        for device_name, results in test_results.items():
            device_score = 0
            device_tests = 0
            
            for test_name, test_result in results.items():
                if test_name == 'device':
                    continue
                
                if isinstance(test_result, dict) and 'acceptable' in test_result:
                    device_score += 100 if test_result['acceptable'] else 0
                    device_tests += 1
            
            if device_tests > 0:
                total_score += device_score / device_tests
                total_tests += 1
        
        return total_score / total_tests if total_tests > 0 else 0
```

## Best Practices Summary

### Mobile Optimization Checklist

1. **Geometry Optimization:**
   - ✅ Target <25K vertices for mid-range devices
   - ✅ Use LOD (Level of Detail) systems
   - ✅ Optimize mesh topology for mobile GPUs
   - ✅ Remove unnecessary geometry details

2. **Texture Optimization:**
   - ✅ Use power-of-2 dimensions
   - ✅ Limit maximum size to 1024×1024 for mid-range
   - ✅ Apply appropriate compression
   - ✅ Create texture atlases where possible

3. **Material Optimization:**
   - ✅ Limit material count (3-5 for low-end devices)
   - ✅ Avoid expensive shader features on mobile
   - ✅ Use simplified PBR models
   - ✅ Optimize for target GPU architecture

4. **Performance Management:**
   - ✅ Implement adaptive quality systems
   - ✅ Monitor frame rate and thermal state
   - ✅ Optimize for 30fps target on mobile
   - ✅ Implement progressive loading

5. **Battery Optimization:**
   - ✅ Reduce rendering complexity on low battery
   - ✅ Implement thermal throttling
   - ✅ Use efficient rendering techniques
   - ✅ Optimize network usage

### Common Mobile Pitfalls

- **Oversized Textures**: Using desktop-sized textures on mobile
- **Complex Geometry**: Not considering mobile GPU limitations
- **Excessive Materials**: Too many unique materials per scene
- **No Adaptive Quality**: Fixed quality regardless of device performance
- **Poor Thermal Management**: Not considering thermal throttling
- **Network Inefficiency**: Large downloads on cellular connections

## Resources and Tools

### Mobile Testing Tools
- **Xcode Instruments**: iOS performance profiling
- **Android GPU Inspector**: Android GPU debugging
- **Unity Profiler**: Cross-platform performance analysis
- **RenderDoc**: Graphics debugging

### Optimization Libraries
- **Open3D**: Mesh decimation and optimization
- **Draco**: Geometry compression for mobile
- **Basis Universal**: Texture compression
- **meshoptimizer**: Mesh optimization algorithms

### Platform Documentation
- **ARKit Documentation**: iOS AR development
- **ARCore Documentation**: Android AR development
- **Metal Performance Shaders**: iOS GPU optimization
- **Vulkan Mobile Best Practices**: Android GPU optimization

## Next Steps

1. **Advanced AR Features Guide** - Animations, physics, and interactions
2. **Enterprise Deployment Guide** - Large-scale AR content management
3. **Cross-Platform Framework** - Unified AR development tools
4. **AI-Powered Optimization** - Machine learning for automatic optimization
