In a decade, the adaptive quality control of video streaming and the super-resolution (SR) technique have been deeply explored. As edge devices improved to have exceptional processing capability than ever before, streaming users can enhance the received image quality to allow the transmitter to compress the images to save its power or pursue network efficiency. In this sense, this paper proposes a novel dynamic video streaming algorithm that adaptively compresses video chunks at the transmitter and separately enhances the quality at the receiver using SR. In order to allow transmission of video chunks with different compression levels and control of the computation burden, we present the adaptive SR network which is optimized by minimizing the weighted sum of losses extracted from different layer outputs. for dynamic video streaming. In addition, we jointly orchestrate video delivery and resource usage, and the proposed video delivery scheme balances the tradeoff well among the average video quality, the queuing delay, buffering time, transmit power, and computation power. Simulation results show that the proposed scheme pursues the quality-of-services (QoS) of the video streaming better than the adaptive quality control without the cooperation of the transmitter and the receiver and the non-adaptive SR network.
翻译:在十年中,对视频流和超分辨率技术的适应性质量控制进行了深入探索;随着边缘装置的改进,具有前所未有的特殊处理能力,流用户可以提高收到的图像质量,使发报机压缩图像以保存其电力或追求网络效率;从这个意义上讲,本文件提出一种新的动态视频流算法,以适应性压缩发报机的视频块,并单独提高使用SR接收器的接收器的质量;为了能够传输具有不同压缩水平和控制计算负担的视频块,我们展示了适应性SR网络,通过尽量减少从不同层产出中提取的损失的加权总和来优化。此外,我们联合协调视频传送和资源使用,拟议的视频传送计划在平均视频质量、排队延迟、缓冲时间、传输能力以及计算能力之间保持平衡。模拟结果显示,拟议方案在不与发报机和接收器网络合作的情况下,采用比适应性质量控制控制更好的服务质量(QOS)。