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

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 unrespons...

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Bibliographic Details
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
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Summary: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%.
ISSN:1520-9210
1941-0077
DOI:10.1109/TMM.2022.3165381