학술논문

PERM: Neural Adaptive Video Streaming with Multi-path Transmission
Document Type
Conference
Source
IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Computer Communications, IEEE INFOCOM 2020 - IEEE Conference on. :1103-1112 Jul, 2020
Subject
Communication, Networking and Broadcast Technologies
Streaming media
Quality of experience
Quality of service
Bit rate
Bandwidth
Throughput
Multi-path transmission
bitrate adaptation
reinforcement learning
preference awareness
Language
ISSN
2641-9874
Abstract
The multi-path transmission techniques enable multiple paths to maximize resource usage and increase throughput in transmission, which have been installed over mobile devices in recent years. For video streaming applications, compared to the single-path transmission, the multi-path techniques can establish multiple subflows simultaneously to extend the available bandwidth for streaming high-quality videos in mobile devices. Existing adaptive video streaming systems have difficulty in harnessing multi-path scheduling and balancing the tradeoff between the quality of experience (QoE) and quality of service (QoS) concerns. In this paper, we propose an actor-critic network based on Periodical Experience Replay for Multi-path video streaming (PERM). Specifically, PERM employs two actor modules and a critic module: the two actor modules respectively assign the path usage of each subflow and select bitrates for the next chunk of the video, while the critic module predicts the overall objectives. We conduct trace-driven emulation and real-world testbed experiment to examine the performance of PERM, and results show that PERM outperforms state-of-the-art multi-path and single path streaming systems, with an improvement of 10%- 15% on the QoE and QoS metrics.