影視業加速引擎：高效能串流架構解析

use actix_web::{web, App, HttpResponse, HttpServer, Responder};
use tokio::fs::File;
use tokio::io::AsyncReadExt;
use std::sync::Arc;
use tokio::sync::Mutex;
use bytes::{Bytes, BytesMut};

// 用於快取常用影片的結構
struct VideoCache {
    cache: std::collections::HashMap<String, Bytes>,
}

impl VideoCache {
    fn new() -> Self {
        VideoCache {
            cache: std::collections::HashMap::new(),
        }
    }
    
    async fn get_or_load(&mut self, path: &str) -> Result<Bytes, std::io::Error> {
        // 檢查快取中是否已有此影片
        if let Some(data) = self.cache.get(path) {
            return Ok(data.clone());
        }
        
        // 從磁碟讀取影片
        let mut file = File::open(path).await?;
        let mut buffer = BytesMut::with_capacity(file.metadata().await?.len() as usize);
        file.read_to_end(&mut buffer).await?;
        
        let bytes = buffer.freeze();
        // 存入快取
        self.cache.insert(path.to_string(), bytes.clone());
        Ok(bytes)
    }
}

// 分享狀態
struct AppState {
    video_cache: Arc<Mutex<VideoCache>>,
}

// 串流處理函式
async fn stream_video(state: web::Data<AppState>, path: web::Path<String>) -> impl Responder {
    let video_path = format!("videos/{}", path.into_inner());
    
    // 嘗試從快取得影片
    let result = {
        let mut cache = state.video_cache.lock().await;
        cache.get_or_load(&video_path).await
    };
    
    match result {
        Ok(data) => {
            // 設定適當的內容類別
            let content_type = if video_path.ends_with(".mp4") {
                "video/mp4"
            } else if video_path.ends_with(".webm") {
                "video/webm"
            } else {
                "application/octet-stream"
            };
            
            HttpResponse::Ok()
                .content_type(content_type)
                .body(data)
        },
        Err(_) => HttpResponse::NotFound().body("影片不存在")
    }
}

#[actix_web::main]
async fn main() -> std::io::Result<()> {
    // 初始化分享狀態
    let app_state = web::Data::new(AppState {
        video_cache: Arc::new(Mutex::new(VideoCache::new())),
    });
    
    println!("影視串流服務啟動於 http://127.0.0.1:8080");
    
    HttpServer::new(move || {
        App::new()
            .app_data(app_state.clone())
            .route("/stream/{filename}", web::get().to(stream_video))
    })
    .workers(4)  // 設定工作執行緒數量
    .bind("127.0.0.1:8080")?
    .run()
    .await
}

use std::sync::Arc;
use tokio::time::Duration;
use webrtc::api::{APIBuilder, API};
use webrtc::ice_transport::ice_server::RTCIceServer;
use webrtc::peer_connection::configuration::RTCConfiguration;
use webrtc::peer_connection::peer_connection_state::RTCPeerConnectionState;
use webrtc::peer_connection::RTCPeerConnection;
use webrtc::track::track_local::track_local_static_sample::TrackLocalStaticSample;
use webrtc::track::track_local::TrackLocal;
use webrtc::Error;

// WebRTC連線管理器
struct WebRTCManager {
    api: API,
    ice_servers: Vec<RTCIceServer>,
}

impl WebRTCManager {
    fn new() -> Self {
        // 建立API例項
        let api = APIBuilder::new().build();
        
        // 設定STUN/TURN伺服器
        let ice_servers = vec![
            RTCIceServer {
                urls: vec!["stun:stun.l.google.com:19302".to_string()],
                ..Default::default()
            },
        ];
        
        WebRTCManager {
            api,
            ice_servers,
        }
    }
    
    async fn create_connection(&self) -> Result<Arc<RTCPeerConnection>, Error> {
        // 建立連線設定
        let config = RTCConfiguration {
            ice_servers: self.ice_servers.clone(),
            ..Default::default()
        };
        
        // 建立對等連線
        let peer_connection = self.api.new_peer_connection(config).await?;
        
        // 監聽連線狀態變化
        let pc_clone = Arc::clone(&peer_connection);
        tokio::spawn(async move {
            while let Ok(state_change) = pc_clone.on_peer_connection_state_change().await {
                match state_change {
                    RTCPeerConnectionState::Connected => {
                        println!("WebRTC連線已建立");
                    }
                    RTCPeerConnectionState::Disconnected => {
                        println!("WebRTC連線已斷開");
                    }
                    RTCPeerConnectionState::Failed => {
                        println!("WebRTC連線失敗");
                    }
                    RTCPeerConnectionState::Closed => {
                        println!("WebRTC連線已關閉");
                        break;
                    }
                    _ => {}
                }
            }
        });
        
        Ok(peer_connection)
    }
    
    // 建立視訊軌道
    async fn create_video_track(&self) -> TrackLocalStaticSample {
        // 建立視訊軌道
        TrackLocalStaticSample::new(
            "video".to_string(),
            "webcam".to_string(),
            "video/h264".to_string(),
        )
    }
}

#[tokio::main]
async fn main() -> Result<(), Error> {
    // 初始化WebRTC管理器
    let manager = WebRTCManager::new();
    
    // 建立連線
    let peer_connection = manager.create_connection().await?;
    
    // 建立視訊軌道
    let video_track = manager.create_video_track().await;
    
    // 將軌道新增到連線
    let rtp_sender = peer_connection.add_track(Arc::new(video_track)).await?;
    
    // 處理RTP封包
    tokio::spawn(async move {
        let mut rtcp_buf = vec![0u8; 1500];
        while let Ok((n, _)) = rtp_sender.read(&mut rtcp_buf).await {
            println!("讀取到 {} 位元組的RTCP封包", n);
            // 在實際應用中，這裡會處理RTCP封包
        }
    });
    
    println!("WebRTC伺服器已啟動");
    
    // 保持程式執行
    loop {
        tokio::time::sleep(Duration::from_secs(1)).await;
    }
}

import tensorflow as tf
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split

class AdaptiveBitratePredictor:
    def __init__(self):
        # 定義模型架構
        self.model = tf.keras.Sequential([
            tf.keras.layers.Dense(64, activation='relu', input_shape=(5,)),
            tf.keras.layers.Dropout(0.2),  # 新增dropout以防止過擬合
            tf.keras.layers.Dense(32, activation='relu'),
            tf.keras.layers.Dense(16, activation='relu'),
            tf.keras.layers.Dense(5, activation='softmax')  # 輸出5個不同位元率的機率
        ])
        
        # 編譯模型
        self.model.compile(
            optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
            loss='sparse_categorical_crossentropy',
            metrics=['accuracy']
        )
        
## 分散式快取技術實作高效內容傳遞

在現代高流量影音串流系統中，降低延遲與伺服器負載是關鍵挑戰。透過整合 Redis 快取與 CDN 分散式架構，我們能顯著提升影片傳遞效能。Rust 語言的高效能非同步 Redis 整合特性，讓常用影片的快取管理變得更為最佳化。

```rust
use redis::Commands;

fn main() {
    let client = redis::Client::open("redis://127.0.0.1/").unwrap();
    let mut con = client.get_connection().unwrap();

    let video_id = "video_123";
    let cache_key = format!("cache:{}", video_id);

    // 將影片中繼資料儲存至 Redis 快取
    let _: () = con.set(&cache_key, "metadata for video 123").unwrap();

    // 從快取中擷取中繼資料
    let metadata: String = con.get(&cache_key).unwrap();
    println!("快取的影片中繼資料: {}", metadata);
}

from sklearn.ensemble import RandomForestClassifier
import numpy as np

# 模擬訓練資料：[觀看時長, 互動率, 結束機率]
X_train = np.array([[5, 0.2, 0.1], [10, 0.5, 0.05], [2, 0.1, 0.3]])  
y_train = np.array([1, 1, 0])  # 1 = 顯示廣告, 0 = 跳過廣告

# 訓練模型
model = RandomForestClassifier()
model.fit(X_train, y_train)

# 為新觀眾預測廣告插入決策
new_viewer = np.array([[7, 0.4, 0.1]])  
show_ad = model.predict(new_viewer)

print(f"顯示廣告: {bool(show_ad[0])}")

use actix_web::{web, App, HttpResponse, HttpServer, Responder};
use tokio::fs::File;
use tokio::io::AsyncReadExt;

async fn stream_video() -> impl Responder {
    let mut file = File::open("video.mp4").await.expect("無法開啟檔案");
    let mut buffer = Vec::new();
    file.read_to_end(&mut buffer).await.expect("無法讀取檔案");

    HttpResponse::Ok()
        .content_type("video/mp4")
        .body(buffer)
}

#[actix_web::main]
async fn main() -> std::io::Result<()> {
    HttpServer::new(|| {
        App::new()
            .route("/stream", web::get().to(stream_video))
    })
    .bind("127.0.0.1:8080")?
    .run()
    .await
}

use webrtc::api::APIBuilder;
use webrtc::peer_connection::configuration::RTCConfiguration;
use webrtc::peer_connection::RTCPeerConnection;
use webrtc::ice_transport::ice_candidate::RTCIceCandidate;

#[tokio::main]
async fn main() {
    let api = APIBuilder::new().build();
    let config = RTCConfiguration::default();
    let peer_connection = api.new_peer_connection(config).await.unwrap();

    let candidate = RTCIceCandidate::new(
        "candidate:1 1 UDP 2122260223 192.168.1.1 3478 typ host".to_string(),
        "0".to_string(),
    )
    .unwrap();
    
    peer_connection.add_ice_candidate(candidate).await.unwrap();

    println!("WebRTC 連線已建立!");
}

use redis::Commands;

fn main() {
    // 建立Redis連線
    let client = redis::Client::open("redis://127.0.0.1/").unwrap();
    let mut con = client.get_connection().unwrap();

    let video_id = "video_123";
    let cache_key = format!("cache:{}", video_id);

    // 將影片元資料存入Redis快取
    let _: () = con.set(&cache_key, "metadata for video 123").unwrap();

    // 從快取中取出元資料
    let metadata: String = con.get(&cache_key).unwrap();
    println!("快取的影片元資料: {}", metadata);
}

import tensorflow as tf
import numpy as np

# 定義AI模型架構
model = tf.keras.Sequential([
    tf.keras.layers.Dense(32, activation='relu', input_shape=(2,)),
    tf.keras.layers.Dense(16, activation='relu'),
    tf.keras.layers.Dense(1, activation='linear')  # 預測位元率
])

# 編譯模型
model.compile(optimizer='adam', loss='mse')

# 模擬訓練資料: [網路速度, 目前位元率]
X_train = np.array([[10, 3000], [5, 2000], [2, 1000]])  
y_train = np.array([4000, 2500, 1200])  # 目標位元率

# 訓練模型
model.fit(X_train, y_train, epochs=100, verbose=0)

# 為新使用者預測位元率
new_user = np.array([[8, 2500]])  
predicted_bitrate = model.predict(new_user)

print(f"預測位元率: {predicted_bitrate[0][0]} kbps")

use wasm_bindgen::prelude::*;

#[wasm_bindgen]
pub fn decode_video(data: &[u8]) -> Vec<u8> {
    // 這裡會呼叫實際的影片解碼函式
    let decoded = some_video_decoding_function(data);
    decoded
}

import tensorflow as tf
import numpy as np

# 定義AI模型架構 - 結合CNN和LSTM
model = tf.keras.Sequential([
    tf.keras.layers.Conv1D(32, kernel_size=3, activation='relu', input_shape=(10, 1)),
    tf.keras.layers.LSTM(32, return_sequences=True),
    tf.keras.layers.LSTM(16),
    tf.keras.layers.Dense(1, activation='linear')  # 預測頻寬
])

# 編譯模型
model.compile(optimizer='adam', loss='mse')

# 模擬訓練資料: 10秒頻寬歷史記錄
X_train = np.random.rand(1000, 10, 1) * 10  # 模擬速度(Mbps)
y_train = np.mean(X_train, axis=1) * 1.1  # 未來頻寬預測(加10%變異)

# 訓練模型
model.fit(X_train, y_train, epochs=50, verbose=0)

# 預測未來頻寬
test_input = np.random.rand(1, 10, 1) * 10
predicted_bandwidth = model.predict(test_input)

print(f"預測頻寬: {predicted_bandwidth[0][0]} Mbps")

use std::process::Command;

fn adjust_bitrate(bitrate: u32) {
    let output = Command::new("ffmpeg")
        .args([
            "-i", "input_video.mp4",
            "-c:v", "libx265",
            "-b:v", &format!("{}k", bitrate),
            "-preset", "fast",
            "output_video.mp4",
        ])
        .output()
        .expect("Failed to execute FFmpeg");

    if output.status.success() {
        println!("Video transcoded to {} kbps", bitrate);
    } else {
        eprintln!("Error in transcoding: {:?}", output);
    }
}

fn main() {
    let predicted_bitrate = 3000;  // AI-derived bitrate value
    adjust_bitrate(predicted_bitrate);
}

from sklearn.ensemble import RandomForestClassifier
import numpy as np

# Simulated training data: [Screen size (inches), Processing power (GHz)]
X_train = np.array([[5.5, 2.0], [13, 3.5], [6.7, 2.5], [15.6, 4.0]])
y_train = np.array(["H.265 720p", "AV1 1080p", "H.265 1080p", "AV1 4K"])  # Recommended codec

# Train AI model
model = RandomForestClassifier()
model.fit(X_train, y_train)

# Predict best codec for a new device
new_device = np.array([[6.5, 2.2]])  # Example: Smartphone
predicted_codec = model.predict(new_device)

print(f"Recommended codec: {predicted_codec[0]}")

use redis::Commands;

fn cache_video(video_id: &str, transcoded_data: &str) {
    let client = redis::Client::open("redis://127.0.0.1/").unwrap();
    let mut con = client.get_connection().unwrap();

    let cache_key = format!("video_cache:{}", video_id);
    let _: () = con.set(&cache_key, transcoded_data).unwrap();

    println!("Cached video: {}", video_id);
}

fn retrieve_video(video_id: &str) -> Option<String> {
    let client = redis::Client::open("redis://127.0.0.1/").unwrap();
    let mut con = client.get_connection().unwrap();

    let cache_key = format!("video_cache:{}", video_id);
    con.get(&cache_key).ok()
}

fn main() {
    let video_id = "12345";
    let transcoded_video = "AV1 1080p";  // Simulated encoded video metadata

    cache_video(video_id, transcoded_video);

    if let Some(cached_data) = retrieve_video(video_id) {
        println!("Retrieved from cache: {}", cached_data);
    } else {
        println!("Cache miss, fetching from disk...");
    }
}

use wasm_bindgen::prelude::*;

#[wasm_bindgen]
pub fn decode_video(data: &[u8]) -> Vec<u8> {
    let decoded = some_video_decoding_function(data);
    decoded
}

from sklearn.ensemble import RandomForestClassifier
import numpy as np

# 模擬訓練資料: [觀看時間(分鐘), 互動率, 結束機率]
X_train = np.array([[5, 0.2, 0.1], [10, 0.5, 0.05], [2, 0.1, 0.3], [8, 0.4, 0.15]])
y_train = np.array([1, 1, 0, 1])  # 1 = 顯示廣告, 0 = 略過廣告

# 訓練模型
model = RandomForestClassifier()
model.fit(X_train, y_train)

# 預測新使用者的廣告插入時機
new_viewer = np.array([[7, 0.3, 0.1]])  
show_ad = model.predict(new_viewer)

print(f"顯示廣告: {bool(show_ad[0])}")

use std::fs::File;
use std::io::{Read, Write};
use std::process::Command;

fn insert_ad(video_file: &str, ad_file: &str, output_file: &str) {
    let output = Command::new("ffmpeg")
        .args([
            "-i", video_file,
            "-i", ad_file,
            "-filter_complex", "[0:v:0][0:a:0][1:v:0][1:a:0] concat=n=2:v=1:a=1 [v][a]",
            "-map", "[v]",
            "-map", "[a]",
            output_file,
        ])
        .output()
        .expect("無法執行FFmpeg");

    if output.status.success() {
        println!("廣告成功插入至 {}", output_file);
    } else {
        eprintln!("廣告插入錯誤: {:?}", output);
    }
}

fn main() {
    let video = "content.mp4";
    let ad = "ad.mp4";
    let output = "output_with_ad.mp4";

    insert_ad(video, ad, output);
}

from sklearn.neighbors import KNeighborsClassifier
import numpy as np

# 模擬訓練資料: [使用者年齡, 觀看類別(0=運動, 1=電影, 2=科技)]
X_train = np.array([[25, 0], [40, 1], [18, 2], [30, 0], [35, 1]])
y_train = np.array(["Nike廣告", "Netflix廣告", "Apple廣告", "Adidas廣告", "HBO廣告"])  # 推薦廣告

# 訓練AI模型
model = KNeighborsClassifier(n_neighbors=1)
model.fit(X_train, y_train)

# 預測新使用者的最佳廣告
new_user = np.array([[28, 0]])  # 範例: 28歲運動愛好者
predicted_ad = model.predict(new_user)

print(f"推薦廣告: {predicted_ad[0]}")

use redis::Commands;

fn track_ad_performance(ad_id: &str, clicks: u32, impressions: u32) {
    let client = redis::Client::open("redis://127.0.0.1/").unwrap();
    let mut con = client.get_connection().unwrap();

    let key = format!("ad_performance:{}", ad_id);
    let _: () = con.hset(&key, "clicks", clicks).unwrap();
    let _: () = con.hset(&key, "impressions", impressions).unwrap();

    println!("已追蹤廣告效能: {}", ad_id);
}

fn retrieve_ad_performance(ad_id: &str) {
    let client = redis::Client::open("redis://127.0.0.1/").unwrap();
    let mut con = client.get_connection().unwrap();

    let key = format!("ad_performance:{}", ad_id);
    let clicks: u32 = con.hget(&key, "clicks").unwrap_or(0);
    let impressions: u32 = con.hget(&key, "impressions").unwrap_or(0);

    println!("廣告 {} - 點選數: {}, 曝光數: {}", ad_id, clicks, impressions);
}

fn main() {
    let ad_id = "Nike_123";
    track_ad_performance(ad_id, 100, 500);
    retrieve_ad_performance(ad_id);
}

# 使用 Rust 官方映像檔
FROM rust:1.72

# 設定工作目錄
WORKDIR /app

# 複製 Rust 專案
COPY . .

# 建置應用程式
RUN cargo build --release

# 開放串流埠
EXPOSE 8080

# 執行 Rust 串流伺服器
CMD ["./target/release/rust-streaming-server"]

apiVersion: apps/v1
kind: Deployment
metadata:
  name: rust-streaming-deployment
spec:
  replicas: 2
  selector:
    matchLabels:
      app: rust-streaming
  template:
    metadata:
      labels:
        app: rust-streaming
    spec:
      containers:
      - name: rust-streaming
        image: myregistry.com/rust-streaming:latest
        ports:
        - containerPort: 8080
        resources:
          limits:
            cpu: "1"
            memory: "512Mi"
          requests:
            cpu: "500m"
            memory: "256Mi"

global
    log stdout format raw local0
    maxconn 50000

defaults
    timeout connect 5000ms
    timeout client 50000ms
    timeout server 50000ms

frontend http_front
    bind *:80
    default_backend video_streaming

backend video_streaming
    balance leastconn
    server stream1 192.168.1.100:8080 check
    server stream2 192.168.1.101:8080 check

use redis::{Commands, RedisResult};

fn cache_video_metadata(video_id: &str, metadata: &str) -> RedisResult<()> {
    let client = redis::Client::open("redis://127.0.0.1/")?;
    let mut con = client.get_connection()?;

    let cache_key = format!("video:metadata:{}", video_id);
    con.set_ex(cache_key, metadata, 3600)?;  // 快取一小時

    Ok(())
}

fn get_cached_video_metadata(video_id: &str) -> Option<String> {
    let client = redis::Client::open("redis://127.0.0.1/").unwrap();
    let mut con = client.get_connection().unwrap();

    let cache_key = format!("video:metadata:{}", video_id);
    con.get(cache_key).ok()
}

fn main() {
    let video_id = "video123";
    let metadata = "Resolution: 1080p, Codec: H.265";

    cache_video_metadata(video_id, metadata).expect("快取影片失敗");

    if let Some(cached_metadata) = get_cached_video_metadata(video_id) {
        println!("從快取中擷取: {}", cached_metadata);
    } else {
        println!("快取未命中，從儲存中擷取...");
    }
}

import requests

CLOUDFLARE_API_KEY = "your_api_key"
ZONE_ID = "your_zone_id"

def purge_video_cache(video_url):
    url = f"https://api.cloudflare.com/client/v4/zones/{ZONE_ID}/purge_cache"
    headers = {
        "Authorization": f"Bearer {CLOUDFLARE_API_KEY}",
        "Content-Type": "application/json"
    }
    data = {"files": [video_url]}
    response = requests.post(url, headers=headers, json=data)

    if response.status_code == 200:
        print(f"已清除 {video_url} 的快取")
    else:
        print(f"清除快取失敗: {response.json()}")

# 使用範例
purge_video_cache("https://cdn.example.com/videos/video123.mp4")

apiVersion: autoscaling/v1
kind: HorizontalPodAutoscaler
metadata:
  name: rust-streaming-hpa
spec:
  maxReplicas: 10
  minReplicas: 2
  targetCPUUtilizationPercentage: 70

import numpy as np

# 狀態空間：[低負載, 中負載, 高負載]
states = ["Low", "Medium", "High"]

# 動作空間：[縮減, 維持, 擴充套件]
actions = ["Down", "Maintain", "Up"]

# Q-table初始化
q_table = np.zeros((len(states), len(actions)))

# 模擬學習迴圈
learning_rate = 0.1
discount_factor = 0.9

for _ in range(1000):  
    current_state = np.random.choice(len(states))  
    action = np.random.choice(len(actions))  

    # 獎勵函式：鼓勵在高負載時擴充套件
    if current_state == 2 and action == 2:  
        reward = 10  
    elif current_state == 0 and action == 0:  
        reward = 5  
    else:  
        reward = -5  

    q_table[current_state, action] += learning_rate * (reward + discount_factor * np.max(q_table[current_state]) - q_table[current_state, action])

# 最佳擴充套件決策
current_load = 2  # 模擬高負載
optimal_action = actions[np.argmax(q_table[current_load])]
print(f"Optimal scaling action: {optimal_action}")

use webrtc::api::APIBuilder;
use webrtc::peer_connection::configuration::RTCConfiguration;
use webrtc::peer_connection::RTCPeerConnection;
use webrtc::ice_transport::ice_candidate::RTCIceCandidate;

#[tokio::main]
async fn main() {
    let api = APIBuilder::new().build();
    let config = RTCConfiguration::default();
    let peer_connection = api.new_peer_connection(config).await.unwrap();

    let candidate = RTCIceCandidate::new(
        "candidate:1 1 UDP 2122260223 192.168.1.1 3478 typ host".to_string(),
        "0".to_string(),
    )
    .unwrap();
    
    peer_connection.add_ice_candidate(candidate).await.unwrap();

    println!("WebRTC P2P connection established!");
}

use wasm_bindgen::prelude::*;

#[wasm_bindgen]
pub fn upscale_video(frame_data: &[u8]) -> Vec<u8> {
    let enhanced_data = super_resolution_algorithm(frame_data);
    enhanced_data
}

use actix_web::{web, App, HttpResponse, HttpServer, Responder};

async fn process_video() -> impl Responder {
    HttpResponse::Ok().body("Edge-processed video data")
}

#[actix_web::main]
async fn main() -> std::io::Result<()> {
    HttpServer::new(|| {
        App::new()
            .route("/edge-process", web::get().to(process_video))
    })
    .bind("0.0.0.0:9000")?
    .run()
    .await
}

use actix_web::{web, App, HttpResponse, HttpServer, Responder};
use tokio::fs::File;
use tokio::io::AsyncReadExt;

async fn stream_video() -> impl Responder {
    let mut file = File::open("video.mp4").await.expect("Failed to open file");
    let mut buffer = Vec::new();
    file.read_to_end(&mut buffer).await.expect("Failed to read file");

    HttpResponse::Ok()
        .content_type("video/mp4")
        .body(buffer)
}

#[actix_web::main]
async fn main() -> std::io::Result<()> {
    HttpServer::new(|| {
        App::new()
            .route("/stream", web::get().to(stream_video))
    })
    .bind("127.0.0.1:8080")?
    .run()
    .await
}

use webrtc::api::APIBuilder;
use webrtc::peer_connection::configuration::RTCConfiguration;
use webrtc::peer_connection::RTCPeerConnection;
use webrtc::ice_transport::ice_candidate::RTCIceCandidate;

#[tokio::main]
async fn main() {
    let api = APIBuilder::new().build();
    let config = RTCConfiguration::default();
    let peer_connection = api.new_peer_connection(config).await.unwrap();

    let candidate = RTCIceCandidate::new(
        "candidate:1 1 UDP 2122260223 192.168.1.1 3478 typ host".to_string(),
        "0".to_string(),
    )
    .unwrap();
    
    peer_connection.add_ice_candidate(candidate).await.unwrap();

    println!("WebRTC connection established!");
}

import tensorflow as tf
import numpy as np

model = tf.keras.Sequential([
    tf.keras.layers.Dense(32, activation='relu', input_shape=(3,)),
    tf.keras.layers.Dense(16, activation='relu'),
    tf.keras.layers.Dense(1, activation='linear')
])

model.compile(optimizer='adam', loss='mse')

X_train = np.array([[10, 1, 3000], [5, 2, 2000], [2, 1, 1000]])  
y_train = np.array([4000, 2500, 1200])  

model.fit(X_train, y_train, epochs=100, verbose=0)

new_user = np.array([[8, 1, 2500]])  
predicted_bitrate = model.predict(new_user)

print(f"Predicted Bitrate: {predicted_bitrate[0][0]} kbps")

use std::process::Command;

fn adjust_bitrate(bitrate: u32) {
    let output = Command::new("ffmpeg")
        .args([
            "-i", "input_video.mp4",
            "-c:v", "libx265",
            "-b:v", &format!("{}k", bitrate),
            "-preset", "fast",
            "output_video.mp4",
        ])
        .output()
        .expect("Failed to execute FFmpeg");

    if output.status.success() {
        println!("Video transcoded to {} kbps", bitrate);
    } else {
        eprintln!("Error in transcoding: {:?}", output);
    }
}

from sklearn.ensemble import RandomForestClassifier
import numpy as np

X_train = np.array([[5, 0.2, 0.1], [10, 0.5, 0.05], [2, 0.1, 0.3], [8, 0.4, 0.15]])
y_train = np.array([1, 1, 0, 1])  

model = RandomForestClassifier()
model.fit(X_train, y_train)

new_viewer = np.array([[7, 0.3, 0.1]])  
show_ad = model.predict(new_viewer)

print(f"Show Ad: {bool(show_ad[0])}")

apiVersion: autoscaling/v1
kind: HorizontalPodAutoscaler
metadata:
  name: rust-streaming-hpa
spec:
  maxReplicas: 10
  minReplicas: 2
  targetCPUUtilizationPercentage: 70