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In light of the brisk tempo characterizing contemporary lifestyles and the escalating burden of diverse stressors, the decline in the quality of individuals' sleep has emerged as a consequential issue exerting a notable impact on human physiological health. This article introduces the EdgeGAN system, which proposes a hybrid architecture for medical smart beds aimed at proficiently monitoring sleep quality. The EdgeGAN system seamlessly integrates the Internet of Things (IoT) and edge computing through the incorporation of lightweight Generative Adversarial Networks (GAN) into edge computing devices. The amalgamation of this integration serves to enhance the efficacy of sleep quality monitoring. Relative to conventional sleep monitoring systems, the EdgeGAN system offers reduced computational complexity and streamlined user operation. Furthermore, it adeptly captures long-term temporal dependencies in sleep data, thereby extending the retention time of historical information. It also exhibits exceptional compatibility with sleep monitoring devices. Moreover, the EdgeGAN system possesses the capability to intelligently determine whether to upload pertinent data to the cloud based on user preferences, thereby diminishing reliance on cloud resources. In comparison to traditional cloud platform systems, the EdgeGAN system proposed in this article has the capability to circumvent data blockages arising from increased user requests. This innovation enhances real-time performance and compatibility in sleep monitoring, prioritizing user privacy protection. As a result, it offers an intelligent and convenient solution for the development of future smart medical devices.

Mobile video streaming dominates today's network traffic, and adaptive bitrate (ABR) algorithms have been routinely adopted for transmitting media content across dynamic mobile networks. State-of-the-art ABR algorithms mainly alter video bitrate without considering audio bitrate as they consider the impact on the video negligible due to their small size. However, to bring users an immersive experience, recent content providers have applied high-quality audio with large sizes, like stereophonic sound. Therefore, improper audio bitrate selection will adversely affect video bitrate selection, leading to undesirable audio/video combinations (the highest video quality with the lowest audio quality, and vice versa) and frequent playback interruptions. To address these inefficiencies, we propose a Self-Play reinforcement learning-based Audio-aware ABR algorithm named SPA to learn strategies for audio and video bitrate selections. By learning from explicit goals, SPA can match the actual requirements and attain good performance. By conducting trace-driven and testbed-based experiments, we observe SPA's considerable superiority compared to existing approaches, including reducing the undesirable combinations by up to 34.17× and achieving zero stall time across 88.57% of traces. We also invite 35 volunteers to join a subjective test, and the result shows that 33/35 people consider SPA provides them with a satisfactory viewing experience.