HTTP Adaptive Streaming (HAS) has recently become the de facto choice of today’s streaming providers to perform a smooth video content delivery to the end users. The key technology behind HAS is the adaptive bitrate selection (ABR) algorithm that adaptively selects the best suitable video bitrate based on either throughput or buffer monitoring techniques. In order to fulfill user’s satisfaction, ABRs must be designed to accurately reflect the perceived quality of experience (QoE), which is influenced by the perceptual and technical factors. However, both throughput and buffer only account for the technical factors, leading to the insufficiency of today’s ABRs in demonstrating human perception. Moreover, existing throughput and buffer-based algorithms are slow-responsive to significant network changes and unstable in terms of video quality, as found by recent research efforts. For those reasons, QABR – a novel QoE-based bitrate selection algorithm – is proposed in this paper that combines the underlying network parameters and user’s instantaneous QoE (in accordance with perceptual factors). Experimental results demonstrate that QABR outperforms the referenced baseline algorithm in various evaluation criteria.
|ジャーナル||International Journal of Advanced Trends in Computer Science and Engineering|
|出版ステータス||Published - 2019|
ASJC Scopus subject areas
- コンピュータ サイエンス（その他）