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QoE-Aware 3D Video Streaming via Deep Reinforcement Learning in Software Defined Networking Enabled Mobile Edge Computing
IEEE Transactions on Network Science and Engineering ( IF 6.6 ) Pub Date : 2020-11-25 , DOI: 10.1109/tnse.2020.3038998
Pan Zhou , Yulai Xie , Ben Niu , Lingjun Pu , Zichuan Xu , Hao Jiang , Huawei Huang

With the advancements of wireless network transmission technology, 2D video is hard to satisfy people's requirement for multimedia. Therefore, the high-definition 3D video that can bring a whole new viewing experience is starting to enter people's vision. However, when a tremendously large number of users play 3D video, it puts enormous computational pressure on the cloud server, which incurs high transmission latency. To release the tension, in this articl we consider a promising computing and networking architecture by incorporating Mobile Edge Computing (MEC) and Software-defined Networking (SDN) and propose a novel resource allocation model (RAM) to allocate resources and reduce delay. At the same time, we introduce the Quality of Experience (QoE) Model (QoEM), which uses information collected during 3D video playback to adaptively allocate the rate of future tiles. The model addresses the problem of assigning the best transmission speed to the block in the case of time-varying characteristicfactors during transmission. We propose an Actor-Critic-based deep reinforcement learning algorithm for viewport prediction and QoE optimization, called QoE-AC. For the differential transmission in the playback phase, we use the LSTM network for bandwidth and viewport prediction, while combining the historical information of the blocks into the Actor-Critic network as observations. The network can be adaptively assigned the best transmission speed for future tiles based on observations to maximize QoE. Finally, the experimental results show that the actual performance of the model is much better than other existing 3D video network models. Under different QoE targets, our proposed system can be adapted to all situations and has a 10%-15% performance improvement.

中文翻译:

通过基于软件定义的网络的深度强化学习的QoE感知3D视频流,支持网络边缘移动边缘计算

随着无线网络传输技术的发展,2D视频难以满足人们对多媒体的需求。因此,能够带来全新观看体验的高清3D视频正开始进入人们的视野。但是,当大量用户播放3D视频时,它将对云服务器造成巨大的计算压力,从而导致高传输延迟。为了缓解这种紧张关系,在本文中,我们通过结合移动边缘计算(MEC)和软件定义的网络(SDN)来考虑有前途的计算和网络体系结构,并提出一种新颖的资源分配模型(RAM)来分配资源并减少延迟。同时,我们介绍了体验质量(QoE)模型(QoEM),它使用在3D视频播放期间收集的信息来自适应地分配未来图块的速率。该模型解决了在传输过程中时变特征因子的情况下为块分配最佳传输速度的问题。我们为视口预测和QoE优化提出了一种基于Actor-Critic的深度强化学习算法,称为QoE-AC。对于回放阶段的差分传输,我们将LSTM网络用于带宽和视口预测,同时将块的历史信息合并到Actor-Critic网络中进行观察。可以根据观测结果为网络自适应地分配最佳传输速度,以用于将来的图块,从而最大化QoE。最后,实验结果表明,该模型的实际性能要比其他现有的3D视频网络模型好得多。在不同的QoE目标下,我们提出的系统可以适应所有情况,并且性能提高10%-15%。
更新日期:2020-11-25
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