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CAST: An Intricate-Scene Aware Adaptive Bitrate Approach for Video Streaming via Parallel Training

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Algorithms and Architectures for Parallel Processing (ICA3PP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14490))

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Abstract

Adaptive Bitrate (ABR) algorithms have become increasingly important for delivering high-quality video content over fluctuating networks. Considering the complexity of video scenes, video chunks can be separated into two categories: those with intricate scenes and those with simple scenes. In practice, improving the quality of intricate chunks can lead to more significant improvements in Quality of Experience (QoE) than improving simple chunks. However, current schemes either assign equal priority to all chunks or optimize using a fixed linear-based reward function, making them inadequate for meeting real-world requirements. To tackle these limitations, this paper introduces a novel ABR approach that explicitly considers bitrate adaptation as the primary objective. The proposed approach, CAST (Complex-scene Aware bitrate algorithm via Self-play reinforcemenT learning), leverages the power of parallel computing with multiple agents to train a neural network, aiming to achieve superior video playback quality for intricate scenes while minimizing frequent freezing events. The extensive trace-driven evaluation and subjective test results demonstrate that CAST outperforms existing off-the-shelf schemes.

This work was conducted during the pursuit of a Master’s degree by Weihe Li at Central South University. This work was supported in part by the National Natural Science Foundation of China (62302524, 62132022); in part by the Key Research and Development Program of Hunan under Grant 2022WK2005; in part by the Natural Science Foundation of Hunan Province, China, under Grant 2021JJ30867; and in part by using computing resources at the High Performance Computing Center of Central South University.

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Notes

  1. 1.

    Here, we employ the average chunk rebuffering time as an indicator of the rebuffering experience. This measure is computed by dividing the total rebuffering time by the total number of chunks [19].

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Author information

Authors and Affiliations

  1. University of Edinburgh, Edinburgh, EH8 9YL, UK

    Weihe Li

  2. Central South University, Changsha, 410083, China

    Jiawei Huang, Jingling Liu, Wenlu Zhang & Jianxin Wang

  3. Lancaster University, Lancaster, LA1 4YW, UK

    Yu Liang

  4. Rutgers University, Newark, NJ, 08901, USA

    Wenjun Lyu

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  1. Weihe Li
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  2. Jiawei Huang
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  3. Yu Liang
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  4. Jingling Liu
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  5. Wenlu Zhang
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  6. Wenjun Lyu
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  7. Jianxin Wang
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Correspondence to Jingling Liu .

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Editors and Affiliations

  1. Royal Melbourne Institute of Technology, Melbourne, VIC, Australia

    Zahir Tari

  2. Tianjin University, Tianjin, China

    Keqiu Li

  3. University of Arizona, Tucson, AZ, USA

    Hongyi Wu

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Li, W. et al. (2024). CAST: An Intricate-Scene Aware Adaptive Bitrate Approach for Video Streaming via Parallel Training. In: Tari, Z., Li, K., Wu, H. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2023. Lecture Notes in Computer Science, vol 14490. Springer, Singapore. https://doi.org/10.1007/978-981-97-0859-8_8

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  • DOI: https://doi.org/10.1007/978-981-97-0859-8_8

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