DIA：一种基于深度强化学习的云游戏兴趣感知自适应码率方案
DIA: A Deep Reinforcement Learning-Based Interest-Aware ABR Scheme for Cloud Gaming
AI总结：
针对云游戏高分辨率低延迟帧传输难题，提出基于DRL的DIA兴趣感知ABR方案，通过语义分割等技术实现对象级码率调整，在5G场景下QoE较SOTA算法提升至少20%。
摘要：
云游戏的持续发展已使用户能够在智能手机和平板电脑等瘦客户端上游玩高质量云游戏。然而，如何在无线网络等受限环境下高效传输高分辨率、低延迟的游戏画面帧仍是一项重大挑战。为解决该问题，本文提出一种名为DIA的基于深度强化学习（DRL）的兴趣感知自适应码率（ABR）方案，该方案可在网络受限条件下，为每帧中感知关键对象保障画质，同时为非关键对象动态分配较低分辨率以节省带宽。DIA方案利用现成的语义分割模型实现对象级码率调整，还设计了基于Transformer的特征提取器和兴趣感知奖励函数，使其能识别关键对象并动态调整其码率，该调整过程由用户游戏内移动速度、对象特征和当前网络状况引导，无需眼动追踪器等额外硬件。大量实验表明，在动态无线网络环境中，DIA方案优于现有ABR方案；值得注意的是，在5G场景下，DIA的归一化平均体验质量（QoE）比最先进（SOTA）的ABR算法至少高出20%。
英文摘要：
The continuous evolution of cloud gaming has enabled users to play high-quality cloud games on thin clients, such as smartphones and tablets. However, how to effectively transmit the gameplay frames with high resolution and low latency, especially over wireless networks, remains a significant challenge. To address this challenge, we propose a Deep Reinforcement Learning (DRL)-based Interest-Aware Adaptive Bitrate (ABR) scheme, called DIA, that guarantees the quality of perceptually key objects in each frame, while dynamically allocating lower resolutions for non-key objects to conserve bandwidth under constrained network conditions. By leveraging an off-the-shelf semantic segmentation model, the proposed DIA scheme can adjust the bitrate at the object-level. We also design a transformer-based feature extractor and an interest-aware reward function to enable the proposed DIA scheme to identify key objects and dynamically adjust their bitrates. This adaptation is guided by users' in-game movement speed, object features, and prevailing network conditions and operates without requiring additional hardware, such as gaze trackers. Extensive experiments show that the proposed DIA scheme outperforms existing ABR schemes in dynamic wireless network environments. Notably, DIA achieves at least a 20% higher normalized average Quality of Experience (QoE) than the State-Of-The-Art (SOTA) ABR algorithms in 5G scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
