Balancing Quality of Experience and Traffic Volume in Adaptive Bitrate Streaming

Abstract

Adaptive bitrate (ABR) streaming services have spread with advances in the codec, video streaming, and network technologies. For smooth video playback in ABR streaming services, a video player runs an ABR algorithm, which dynamically adjusts the bitrate of video data on the basis of the statuses of the network and player. Existing ABR algorithms calculate a suitable bitrate to maximize the quality of experience (QoE). However, providing a high-QoE video increases network investment costs and content delivery network (CDN) usage fees. According to a survey conducted by the Streaming Video Alliance, mobile users prefer low-traffic videos to high-QoE videos. To reduce traffic volume, commercial video-streaming services enable users to set an upper limit of the bitrate. However, this cannot always achieve the required QoE because they cannot select a high bitrate even when the communication environment improves during viewing. In this paper, we propose BANQUET, a novel ABR algorithm that can reduce the traffic volume while maintaining QoE above the target QoE. The target QoE can be set by users or streaming providers considering user's preferences or CDN budget. BANQUET selects a suitable bitrate by estimating QoE and traffic volume that will be experienced by all the bitrate patterns for the next several chunks on the basis of future throughput and a buffer transition calculation. The trace-based simulation showed that BANQUET reduces traffic volume 18.3%-51.2% on average in the mobile environment and 1.2%-38.3% in the broadband environment while maintaining QoE the same as or better than existing algorithms.

Keywords

• Adaptive bitrate streaming
• quality of experience
• traffic volume reduction
• video streaming