Deep Reinforcement Learning Based Adaptive 360-degree Video Streaming with Field of View Joint Prediction

Yuanhong Zhang, Zhiwen Wang, Junquan Liu, Haipeng Du, Qinghua Zheng, Weizhan Zhang
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引用次数: 1

Abstract

With the development of 360-degree video and HTTP adaptive streaming (HAS), tile-based adaptive 360-degree video streaming has become a promising paradigm for reducing the bandwidth consumption of delivering the panoramic video content. However, there are two main challenges for the adaptive 360-degree video streaming, accurate long-term prediction of the future field of view (Fo V) and optimal adaptive bitrate (ABR) transmission strategy. In this paper, we propose an attention-based multi-user Fo V joint prediction approach to improve the accuracy, establishing a probability model of watching video tiles for users and applying Long Short-Term Memory (LSTM) network and DBSCAN clustering method. Furthermore, we present an adaptive 360-degree video streaming approach based on deep reinforcement learning (DRL), using A3C algorithm to optimize the QoE. The real-world trace-driven experiments demonstrate that our approach achieves about 8 % gains on user Fo V prediction precision and an increase at least 20 % on user QoE compared with the benchmarks.
基于深度强化学习的自适应360度视频流视场联合预测
随着360度视频和HTTP自适应流媒体技术的发展,基于tile的自适应360度视频流已经成为降低全景视频内容传输带宽消耗的一种很有前途的模式。然而,自适应360度视频流存在两个主要挑战:对未来视场的准确长期预测(Fo V)和最佳自适应比特率(ABR)传输策略。为了提高准确率,本文提出了一种基于注意力的多用户Fo V联合预测方法,建立了用户观看视频片段的概率模型,并应用长短期记忆(LSTM)网络和DBSCAN聚类方法。此外,我们提出了一种基于深度强化学习(DRL)的自适应360度视频流方法,使用A3C算法优化QoE。真实世界的跟踪驱动实验表明,与基准测试相比,我们的方法在用户Fo V预测精度上提高了约8%,在用户QoE上提高了至少20%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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