The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Understanding the impact of video quality on user engagement
Abstract
As the distribution of the video over the Internet becomes main- stream and its consumption moves from the computer to the TV screen, user expectation for high quality is constantly increasing. In this context, it is crucial for content providers to understand if and how video quality affects user engagement and how to best invest their resources to optimize video quality. This paper is a first step towards addressing these questions. We use a unique dataset that spans different content types, including short video on demand (VoD), long VoD, and live content from popular video con- tent providers. Using client-side instrumentation, we measure quality metrics such as the join time, buffering ratio, average bitrate, rendering quality, and rate of buffering events.
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Cites
Determining causes and severity of end-user frustration
Ceaparu, I., Lazar, J., Bessiere, K., Robinson, J., & Shneiderman, B. (2004) · International Journal of Human-Computer Interaction · doi:10.1207/s15327590ijhc1703_3 · Cited by 399
Cited by
A Control-Theoretic Approach for Dynamic Adaptive Video Streaming over HTTP
Yin, X., Jindal, A., Sekar, V., & Sinopoli, B. (2015) · Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication · doi:10.1145/2785956.2787486 · Cited by 1,016
Self-citationA Survey on Quality of Experience of HTTP Adaptive Streaming
Seufert, M., Egger, S., Slanina, M., Zinner, T., Hoßfeld, T., & Tran-Gia, P. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2014.2360940 · Cited by 844
Improving fairness, efficiency, and stability in HTTP-based adaptive video streaming with FESTIVE
Jiang, J., Sekar, V., & Zhang, H. (2012) · Proceedings of the 8th international conference on Emerging networking experiments and technologies · doi:10.1145/2413176.2413189 · Cited by 688
Self-citationCS2P: Improving Video Bitrate Selection and Adaptation with Data-Driven Throughput Prediction
Sun, Y., Yin, X., Jiang, J., Sekar, V., Lin, F., Wang, N., Liu, T., & Sinopoli, B. (2016) · Proceedings of the 2016 ACM SIGCOMM Conference · doi:10.1145/2934872.2934898 · Cited by 407
Self-citationTowards network-wide QoE fairness using openflow-assisted adaptive video streaming
Georgopoulos, P., Elkhatib, Y., Broadbent, M., Mu, M., & Race, N. (2013) · Proceedings of the 2013 ACM SIGCOMM workshop on Future human-centric multimedia networking · doi:10.1145/2491172.2491181 · Cited by 242
Metadata
- Authors
- Dobrian, F., Sekar, V., Awan, A., Stoica, I., Joseph, D., Ganjam, A., Zhan, J., & Zhang, H.
- Venue
- Proceedings of the ACM SIGCOMM 2011 conference
- Publisher
- ACM
- Published
- 2011-08-15
- Pages
- 362-373
- OpenAlex
- W2013628864
- Cited by
- 673
- References
- 1
- FWCI
- 36.35
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Dobrian, F., Sekar, V., Awan, A., Stoica, I., Joseph, D., Ganjam, A., Zhan, J., & Zhang, H. (2011). Understanding the impact of video quality on user engagement. Proceedings of the ACM SIGCOMM 2011 conference. https://doi.org/10.1145/2018436.2018478
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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