Android 基于 WebRTC 的高清语音通话实现与性能优化

2. JNI 层音频处理

关键环形缓冲区设计（C++ 实现）：

class AudioBuffer {
public:
    AudioBuffer(int size) : buffer(size), read_pos(0), write_pos(0) {}

    void write(const int16_t* data, size_t len) {
        std::lock_guard<std::mutex> lock(mutex);
        for (size_t i = 0; i < len; ++i) {
            buffer[write_pos] = data[i];
            write_pos = (write_pos + 1) % buffer.size();
        }
    }

    size_t read(int16_t* data, size_t len) {
        std::lock_guard<std::mutex> lock(mutex);
        size_t read_len = 0;
        while (read_pos != write_pos && read_len < len) {
            data[read_len++] = buffer[read_pos];
            read_pos = (read_pos + 1) % buffer.size();
        }
        return read_len;
    }

private:
    std::vector<int16_t> buffer;
    size_t read_pos, write_pos;
    std::mutex mutex;
};

3. 性能监控实现

关键指标采集方案：

class CallQualityMonitor {
    private val statsTimer = Timer()
    private var totalPackets = 0
    private var lostPackets = 0

    fun startMonitoring(pc: PeerConnection) {
        statsTimer.scheduleAtFixedRate(object : TimerTask() {
            override fun run() {
                pc.getStats { reports ->
                    reports.forEach { report ->
                        when (report.type) {
                            "outbound-rtp" -> {
                                totalPackets = report.stat("packetsSent")?.toInt() ?: 0
                                lostPackets = report.stat("packetsLost")?.toInt() ?: 0
                            }
                        }
                    }
                }
            }
        }, 0, 5000)
    }
}

避坑指南

• 在 AndroidManifest.xml 中声明 FOREGROUND_SERVICE 权限
• 使用 WakeLock 保持 CPU 唤醒：

val wakeLock = powerManager.newWakeLock(
    PowerManager.PARTIAL_WAKE_LOCK,
    "MyApp::AudioWakeLock"
).apply { acquire() }

• 强制设置采样率为 48kHz：

AudioManager.setParameters("audio_param;sampling_rate=48000")

• 双扬声器设备需要调整 AEC 参数：

webrtc::EchoCanceller3Config config;
config.delay.down_sampling_factor = 4;
config.filter.main.length_blocks = 12;

优化方向与思考

经过实测，我们实现了端到端延迟控制在 200ms 内的目标。但仍有优化空间：

• 如何结合 ML 预测网络质量实现动态 FEC？
• 尝试修改 opus 编码参数：复杂度 (1-10)、帧大小 (2.5-60ms)、比特率 (6-510kbps)

建议读者可以尝试调整这些参数，观察对通话质量的影响。例如：

MediaConstraints audioConstraints = new MediaConstraints();
audioConstraints.mandatory.add(
    new MediaConstraints.KeyValuePair("googOpusComplexity", "8")
);