Abstarct

Currently video streaming in heterogeneous network environments is affected by limited network bandwidth availability and consequent low and variable user Quality of Experience (QoE) levels. In particular, for the case of live video streaming, a very high number of end-clients request content at the same time, generating huge concurrent traffic, and putting pressure on the existing network infrastructure. An approach which helps address this issue is deployment of emerging edge computing technologies to smooth the live streaming traffic and improve QoE by adapting client bitrates and caching content at the edge server. In this context, this paper proposes a novel Q oE-aware A daptive V ideo bitrate A ggregation scheme for HTTP live streaming based on smart edge computing (QAVA). As an intelligent proxy server, a “smart edge” which deploys QAVA aggregates all the traffic requested by clients for the same live streaming service and adapts their bitrates based on network conditions, client states and video characteristics. The adaptation is performed based on a Deep Reinforcement Learning (DRL)-based algorithm, which is also proposed. The QAVA DRL algorithm is trained and modeled based on a real client experience dataset. The experimental evaluation results presented in this paper show how QAVA outperforms other state-of-the-art adaptive bitrate algorithms in terms of average QoE and QoE fairness.

Problem to solve

Improve user Quality of Experience (QoE) for the case of live video streaming.

Method

This paper proposes QAVA, a smart QoE-aware adaptive video bitrate aggregation scheme for HTTP live streaming based on edge computing. QAVA is deployed at the edge nodes of an access network where bandwidth competition mostly happens [16]. By monitoring network performance and availing from edge storage and computation, QAVA provides live video services to all the clients within the same access network, at improved QoE levels. Specifically, QAVA first aggregates the demands for the same video from end clients, then requests the content at an appropriate bitrate from data centers, and finally delivers it to the clients. However, QAVA needs to overcome variations in network conditions, diversity of client behaviors and characteristics, and difficulty in controlling client QoE. In order to address these, QAVA employs a Deep Reinforcement Learning (DRL)-based control policy to adjust video bitrate selections intelligently in real-time based on network conditions, client states, and video characteristics.

Result

In order to assess the performance of QAVA, a prototype based on Nginx, uWSGI and Django is employed. The performance of QAVA is evaluated under different network conditions. The results show how, when compared with several state-of-the-art ABR approaches based on the edge nodes, QAVA improves average QoE by between 7% and 64% and QoE fairness by between 19% and 52%.

Bibtex

@article{ma2022qava,
  title={QAVA: QoE-aware adaptive video bitrate aggregation for HTTP live streaming based on smart edge computing},
  author={Ma, Xiaoteng and Li, Qing and Zou, Longhao and Peng, Junkun and Zhou, Jianer and Chai, Jimeng and Jiang, Yong and Muntean, Gabriel-Miro},
  journal={IEEE Transactions on Broadcasting},
  volume={68},
  number={3},
  pages={661--676},
  year={2022},
  publisher={IEEE}
}