In this paper, we present Kairos, a model predictive control (MPC)-based adaptive bitrate (ABR) scheme that integrates streaming-aware throughput predictions to enhance video streaming quality. Kairos features an attention-based throughput predictor with buffer-aware uncertainty control, improving prediction accuracy and adaptability to network conditions. Specifically, we introduce a multi-time attention network to handle the irregularly sampled sequences in streaming data, creating uniformly spaced latent representations. Additionally, we design a separate prediction network that estimates future throughput at multiple percentiles and incorporates a buffer-aware uncertainty adjustment module. This module dynamically selects the appropriate throughput percentile based on the buffer size, enhancing robustness to varying network conditions. Lastly, to mitigate QoE smoothness penalties caused by predictors focused solely on accuracy, we introduce a smoothness regularizer. By embedding streaming-aware characteristics, such as sampling irregularity, buffer occupancy, and smoothness, into the throughput predictor design, Kairos significantly improves bitrate decision-making within the MPC framework. Extensive trace-driven and real-world experiments demonstrate that Kairos outperforms state-of-the-art ABR schemes, achieving an average QoE improvement of 1.52% to 7.28% under various network conditions.

翻译：本文提出Kairos，一种基于模型预测控制的自适应码率方案，通过集成流感知吞吐量预测来提升视频流传输质量。Kairos采用基于注意力的吞吐量预测器，配备缓冲区感知的不确定性控制机制，从而提升预测精度及对网络条件的适应能力。具体而言，我们引入多时间注意力网络处理流数据中非均匀采样的序列，生成均匀间隔的潜在表征。此外，我们设计了独立的预测网络，用于估计多个百分位点的未来吞吐量，并集成缓冲区感知的不确定性调节模块。该模块根据缓冲区大小动态选择适当的吞吐量百分位点，增强了对多变网络条件的鲁棒性。最后，为减轻仅关注精度的预测器对服务质量平滑性造成的惩罚，我们引入了平滑性正则化器。通过将采样非均匀性、缓冲区占用状态和平滑性等流感知特征嵌入吞吐量预测器设计，Kairos显著提升了模型预测控制框架内的码率决策性能。大量轨迹驱动实验与真实环境实验表明，Kairos在多种网络条件下均优于现有最优的自适应码率方案，平均服务质量提升幅度达1.52%至7.28%。