User-space Adaptive Bitrate (ABR) algorithms cannot see the transport layer signals that matter most, such as minimum RTT and instantaneous delivery rate, and they respond to network changes only after damage has already propagated to the playout buffer. We present eBandit, a framework that relocates both network monitoring and ABR algorithm selection into the Linux kernel using eBPF. A lightweight epsilon-greedy Multi-Armed Bandit (MAB) runs inside a sockops program, evaluating three ABR heuristics against a reward derived from live TCP metrics. On an adversarial synthetic trace eBandit achieves $416.3 \pm 4.9$ cumulative QoE, outperforming the best static heuristic by $7.2\%$. On 42 real-world sessions eBandit achieves a mean QoE per chunk of $1.241$, the highest across all policies, demonstrating that kernel-resident bandit learning transfers to heterogeneous mobile conditions.

翻译：用户空间自适应比特率算法无法观测到传输层最关键的信号（如最小往返时延和瞬时递送速率），且仅在损伤已传播至播放缓冲区后才响应网络变化。我们提出eBandit框架，通过eBPF将网络监测和自适应比特率算法选择功能迁移至Linux内核。轻量级ε-贪婪多臂老虎机运行在sockops程序中，基于实时TCP度量生成的奖励对三种自适应比特率启发式算法进行评估。在对抗性合成轨迹上，eBandit实现了$416.3 \pm 4.9$的累积体验质量，超越最佳静态启发式算法$7.2\%$。在42个真实网络会话中，eBandit每块的体验质量均值达到1.241，优于所有其他策略，证明内核常驻老虎机学习可迁移至异构移动网络环境。