Qrator: An Interest-Aware Approach to ABR Streaming Based on User Engagement
Qrator: An Interest-Aware Approach to ABR Streaming Based on User Engagement
复制标题
Qrator：基于用户参与度的 ABR 流媒体兴趣感知方法
作者：
Dongkeun Lee;Minwoo Joo;Wonjun Lee
传统的自适应比特率 (ABR) 流媒体方法无法进一步提高体验质量 (QoE)，因为它们仅关注网络状况，对视频语义和用户行为漠不关心。这些 ABR 方案无法提高感知视频质量，因为它们忽略了用户对视频部分的不同兴趣度 (DoI)，并且可能会由于用户行为（例如选择性观看感兴趣的部分）引起的频繁重新缓冲而导致 QoE 下降。为此，本文提出了Qrator，这是首次尝试利用时间戳和点赞作为用户参与度数据来推断用户的真实兴趣，进一步提升用户感知的QoE。基于视频评论中的时间戳和点赞，Qrator 在执行兴趣感知速率自适应和预取时考虑 DoI 变化和用户行为，从而提高整体 QoE。 Qrator 可以广泛应用于传统的 ABR 方法，而无需显着修改其实现。评测结果表明，Qrator能够在不降低平均码率的情况下，提高用户感兴趣部分的码率。此外，在用户行为模式下应用 Qrator 可以将重新缓冲比率和重新缓冲数量分别减少 32% 和 31%，同时将其他 QoE 指标保持在类似程度。
Conventional approaches to adaptive bitrate (ABR) streaming fail to further improve the quality of experience (QoE) as they are indifferent to video semantics and user behaviors by focusing solely on the network conditions. These ABR schemes cannot enhance the perceptual video quality as they neglect the users’ varying degree of interest (DoI) over video sections and may experience QoE degradation due to frequent rebuffers caused by user behaviors such as selectively watching the interesting sections. To this end, this article proposes Qrator, which is the very first attempt to utilize timestamps and likes as user engagement data to infer users’ genuine interests and further elevate the user-perceived QoE. Based on timestamps and likes in video comments, Qrator improves the overall QoE by considering DoI variations and user behaviors in performing interest-aware rate adaptation and prefetching. Qrator can be widely applied to conventional ABR approaches without significantly modifying their implementations. Evaluation results show that Qrator can heighten the bitrates of user interest sections without degrading the average bitrate. Furthermore, applying Qrator under user behavior patterns can reduce the rebuffering ratio and the number of rebuffers by 32% and 31%, respectively, while maintaining other QoE metrics to a similar extent.