StreamOptix: A Cross-layer Adaptive Video Delivery Scheme
<|reference_start|>StreamOptix: A Cross-layer Adaptive Video Delivery Scheme: This paper presents a cross-layer video delivery scheme, StreamOptix, and proposes a joint optimization algorithm for video delivery that leverages the characteristics of the physical (PHY), medium access control (MAC), and application (APP) layers. Most existing methods for optimizing video transmission over different layers were developed individually. Realizing a cross-layer design has always been a significant challenge, mainly due to the complex interactions and mismatches in timescales between layers, as well as the presence of distinct objectives in different layers. To address these complications, we take a divide-and-conquer approach and break down the formulated cross-layer optimization problem for video delivery into three sub-problems. We then propose a three-stage closedloop optimization framework, which consists of 1) an adaptive bitrate (ABR) strategy based on the link capacity information from PHY, 2) a video-aware resource allocation scheme accounting for the APP bitrate constraint, and 3) a link adaptation technique utilizing the soft acknowledgment feedback (soft-ACK). The proposed framework also supports the collections of the distorted bitstreams transmitted across the link. This allows a more reasonable assessment of video quality compared to many existing ABR methods that simply neglect the distortions occurring in the PHY layer. Experiments conducted under various network settings demonstrate the effectiveness and superiority of the new cross-layer optimization strategy. A byproduct of this study is the development of more comprehensive performance metrics on video delivery, which lays down the foundation for extending our system to multimodal communications in the future. Code for reproducing the experimental results is available at https://github.com/Evan-sudo/StreamOptix.<|reference_end|>
@article{liu2024streamoptix:,
    title={StreamOptix: A Cross-layer Adaptive Video Delivery Scheme},
    author={Mufan Liu, Le Yang, Yifan Wang, Yiling Xu, Ye-Kui Wang, Yunfeng Guan},
    journal={arXiv preprint arXiv:2406.04632},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04632},
    primaryClass={cs.MM}
}