Timely and Accurate Bitrate Switching in HTTP Adaptive Streaming With Date-Driven I-Frame Prediction

• Published in: IEEE transactions on multimedia 2023, Vol.25, p.3753-3762
• Main Authors: Feng, Tongtong, Qi, Qi, Wang, Jingyu, Liao, Jianxin, Liu, Jiangchuan
• Format: Article
• Language: English
• Subjects: Adaptive algorithms; Bandwidth; Bit rate; bitrate switching; Decisions; HTTP adaptive streaming; I-frame prediction; Quality of experience; reinforcement learning; Segmentation; Segments; Servers; Streaming media; Switches; Switching theory
• ISSN: 1520-9210; 1941-0077
• DOI: 10.1109/TMM.2022.3165381

In today's Internet, bandwidth dynamics are inevitable, and hence, the bitrate for live streaming applications should also be dynamically adjusted. However, in existing HTTP-based adaptive streaming (HAS), bitrate switching can only be performed at segment boundaries, making decisions unresponsive and often inaccurate. In this paper, we start from a close investigation on the impact of the segment length in HAS and accordingly present VHAS , an extension towards intelligent variable-length segmentation, which makes client-side decisions based on the massive amount of real-time information from the network and viewers. VHAS implements a smart trigger mechanism that balances accuracy and overhead for variable-length segmentation. We further develop an adaptive bitrate switching algorithm with data-driven I-frame prediction, which is tailored to individual viewers to minimize bitrate mismatches. We evaluate VHAS via extensive trace-driven simulations, and our results demonstrate that compared with state-of-the-art solutions, VHAS achieves 15%-49% gains in QoE, with a noticeable bandwidth reduction of 37%-57%.