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Llama - Low Latency Adaptive Media Algorithm

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

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Llama - Low Latency Adaptive Media Algorithm. / Lyko, T.; Broadbent, M.; Race, N.; Nilsson, M.; Farrow, P.; Appleby, S.

2020 IEEE International Symposium on Multimedia (ISM). IEEE, 2021. p. 113-121.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Lyko, T, Broadbent, M, Race, N, Nilsson, M, Farrow, P & Appleby, S 2021, Llama - Low Latency Adaptive Media Algorithm. in 2020 IEEE International Symposium on Multimedia (ISM). IEEE, pp. 113-121. https://doi.org/10.1109/ISM.2020.00027

APA

Lyko, T., Broadbent, M., Race, N., Nilsson, M., Farrow, P., & Appleby, S. (2021). Llama - Low Latency Adaptive Media Algorithm. In 2020 IEEE International Symposium on Multimedia (ISM) (pp. 113-121). IEEE. https://doi.org/10.1109/ISM.2020.00027

Vancouver

Lyko T, Broadbent M, Race N, Nilsson M, Farrow P, Appleby S. Llama - Low Latency Adaptive Media Algorithm. In 2020 IEEE International Symposium on Multimedia (ISM). IEEE. 2021. p. 113-121 https://doi.org/10.1109/ISM.2020.00027

Author

Lyko, T. ; Broadbent, M. ; Race, N. ; Nilsson, M. ; Farrow, P. ; Appleby, S. / Llama - Low Latency Adaptive Media Algorithm. 2020 IEEE International Symposium on Multimedia (ISM). IEEE, 2021. pp. 113-121

Bibtex

@inproceedings{9581b86e1c7e4e208e85d4dbfb0e22f2,
title = "Llama - Low Latency Adaptive Media Algorithm",
abstract = "In the recent years, HTTP Adaptive Bit Rate (ABR) streaming including Dynamic Adaptive Streaming over HTTP (DASH) has become the most popular technology for video streaming over the Internet. The client device requests segments of content using HTTP, with an ABR algorithm selecting the quality at which to request each segment to trade-off video quality with the avoidance of stalling. This introduces high latency compared to traditional broadcast methods, mostly in the client buffer which needs to hold enough data to absorb any changes in network conditions. Clients employ an ABR algorithm which monitors network conditions and adjusts the quality at which segments are requested to maximise the user's Quality of Experience. The size of the client buffer depends on the ABR algorithm's capability to respond to changes in network conditions in a timely manner, hence, low latency live streaming requires an ABR algorithm that can perform well with a small client buffer. In this paper, we present Llama - a new ABR algorithm specifically designed to operate in such scenarios. Our new ABR algorithm employs the novel idea of using two independent throughput measurements made over different timescales. We have evaluated Llama by comparing it against four popular ABR algorithms in terms of multiple QoE metrics, across multiple client settings, and in various network scenarios based on CDN logs of a commercial live TV service. Llama outperforms other ABR algorithms, improving the P.1203 Mean Opinion Score (MOS) as well as reducing rebuffering by 33% when using DASH, and 68% with CMAF in the lowest latency scenario.",
keywords = "Quality of experience, Video recording, Streaming media, Quality assessment, Bandwidth, Throughput, Bit rate, live, video, streaming, abr, low, latency",
author = "T. Lyko and M. Broadbent and N. Race and M. Nilsson and P. Farrow and S. Appleby",
note = "{\textcopyright}2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. ",
year = "2021",
month = jan,
day = "22",
doi = "10.1109/ISM.2020.00027",
language = "English",
isbn = "9781728186986",
pages = "113--121",
booktitle = "2020 IEEE International Symposium on Multimedia (ISM)",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Llama - Low Latency Adaptive Media Algorithm

AU - Lyko, T.

AU - Broadbent, M.

AU - Race, N.

AU - Nilsson, M.

AU - Farrow, P.

AU - Appleby, S.

N1 - ©2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2021/1/22

Y1 - 2021/1/22

N2 - In the recent years, HTTP Adaptive Bit Rate (ABR) streaming including Dynamic Adaptive Streaming over HTTP (DASH) has become the most popular technology for video streaming over the Internet. The client device requests segments of content using HTTP, with an ABR algorithm selecting the quality at which to request each segment to trade-off video quality with the avoidance of stalling. This introduces high latency compared to traditional broadcast methods, mostly in the client buffer which needs to hold enough data to absorb any changes in network conditions. Clients employ an ABR algorithm which monitors network conditions and adjusts the quality at which segments are requested to maximise the user's Quality of Experience. The size of the client buffer depends on the ABR algorithm's capability to respond to changes in network conditions in a timely manner, hence, low latency live streaming requires an ABR algorithm that can perform well with a small client buffer. In this paper, we present Llama - a new ABR algorithm specifically designed to operate in such scenarios. Our new ABR algorithm employs the novel idea of using two independent throughput measurements made over different timescales. We have evaluated Llama by comparing it against four popular ABR algorithms in terms of multiple QoE metrics, across multiple client settings, and in various network scenarios based on CDN logs of a commercial live TV service. Llama outperforms other ABR algorithms, improving the P.1203 Mean Opinion Score (MOS) as well as reducing rebuffering by 33% when using DASH, and 68% with CMAF in the lowest latency scenario.

AB - In the recent years, HTTP Adaptive Bit Rate (ABR) streaming including Dynamic Adaptive Streaming over HTTP (DASH) has become the most popular technology for video streaming over the Internet. The client device requests segments of content using HTTP, with an ABR algorithm selecting the quality at which to request each segment to trade-off video quality with the avoidance of stalling. This introduces high latency compared to traditional broadcast methods, mostly in the client buffer which needs to hold enough data to absorb any changes in network conditions. Clients employ an ABR algorithm which monitors network conditions and adjusts the quality at which segments are requested to maximise the user's Quality of Experience. The size of the client buffer depends on the ABR algorithm's capability to respond to changes in network conditions in a timely manner, hence, low latency live streaming requires an ABR algorithm that can perform well with a small client buffer. In this paper, we present Llama - a new ABR algorithm specifically designed to operate in such scenarios. Our new ABR algorithm employs the novel idea of using two independent throughput measurements made over different timescales. We have evaluated Llama by comparing it against four popular ABR algorithms in terms of multiple QoE metrics, across multiple client settings, and in various network scenarios based on CDN logs of a commercial live TV service. Llama outperforms other ABR algorithms, improving the P.1203 Mean Opinion Score (MOS) as well as reducing rebuffering by 33% when using DASH, and 68% with CMAF in the lowest latency scenario.

KW - Quality of experience

KW - Video recording

KW - Streaming media

KW - Quality assessment

KW - Bandwidth

KW - Throughput

KW - Bit rate

KW - live

KW - video

KW - streaming

KW - abr

KW - low

KW - latency

U2 - 10.1109/ISM.2020.00027

DO - 10.1109/ISM.2020.00027

M3 - Conference contribution/Paper

SN - 9781728186986

SP - 113

EP - 121

BT - 2020 IEEE International Symposium on Multimedia (ISM)

PB - IEEE

ER -