Design and Evaluation of a Self-Learning HTTP Adaptive Video Streaming Client
Open accessAbstract
HTTP Adaptive Streaming (HAS) is becoming the de facto standard for Over-The-Top (OTT)-based video streaming services such as YouTube and Netflix. By splitting a video into multiple segments of a couple of seconds and encoding each of these at multiple quality levels, HAS allows a video client to dynamically adapt the requested quality during the playout to react to network changes. However, state-of-the-art quality selection heuristics are deterministic and tailored to specific network configurations. Therefore, they are unable to cope with a vast range of highly dynamic network settings. In this letter, a novel Reinforcement Learning (RL)-based HAS client is presented and evaluated. The self-learning HAS client dynamically adapts its behaviour by interacting with the environment to optimize the Quality of Experience (QoE), the quality as perceived by the end-user. The proposed client has been thoroughly evaluated using a network-based simulator and is shown to outperform traditional HAS clients by up to 13% in a mobile network environment.
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The Q-learning client achieves on average 9.12% higher estimated MOS, with 16.65% lower standard deviation, than the traditional Microsoft ISS Smooth Streaming client.
“The authors claim that the Q-learning client achieves in average 9.12% higher estimated MOS (program-generated), with 16.65% lower standard deviation, than the traditional Microsoft ISS Smooth Streaming (MSS) client.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [102]
Data-driven approaches, in particular ML, have been employed mainly in two ways: to predict changes in network QoS and trigger adaptation to reduce rebuffering time, and to select appropriate adaptation action.
“Data-driven approaches, in particular ML, have been employed mainly in two different ways: (1) to predict changes in network QoS, namely throughput, and trigger adaptation mechanism to reduce rebuffering time [432], and (2) to select appropriate adaptation action [102].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [102]
Some works find it sufficient to correlate QoE to a single network QoS parameter, while others argue that multiple QoS parameters impact QoE and need to be considered in tandem.
“While some find it sufficient to correlate the QoE to a single network QoS parameter [145, 240, 383, 408], e.g.delay or throughput, others argue that multiple QoS parameters impact the QoE and need to be considered in tandem as features in a QoE/QoS correlation model [89,102,114,235,287,328,432].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [102]
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Cites
Dynamic adaptive streaming over HTTP --
Stockhammer, T. (2011) · doi:10.1145/1943552.1943572 · Cited by 1,236 · see citing works
An experimental evaluation of rate-adaptation algorithms in adaptive streaming over HTTP
Akhshabi, S., Begen, A. C., & Dovrolis, C. (2011) · doi:10.1145/1943552.1943574 · Cited by 616 · see citing works
Measuring the quality of experience of HTTP video streaming
Mok, R. K. P., Chan, E. W. W., & Chang, R. K. C. (2011) · doi:10.1109/inm.2011.5990550 · Cited by 478 · see citing works
Commute path bandwidth traces from 3G networks
Riiser, H., Vigmostad, P., Griwodz, C., & Halvorsen, P. (2013) · ACM Multimedia Systems Conference (MMSys) · doi:10.1145/2483977.2483991 · Cited by 367 · see citing works
Adaptation algorithm for adaptive streaming over HTTP
Miller, K., Quacchio, E., Gennari, G., & Adam Wolisz, A. (2012) · International Packet Video Workshop (PV) · doi:10.1109/pv.2012.6229732 · Cited by 238 · see citing works
Model for estimating QoE of video delivered using HTTP adaptive streaming
Vriendt, J. D., Vleeschauwer, D. D., & Robinson, D. C. (2013) · Integrated Network Management · Cited by 85 · see citing works
On the merits of SVC-based HTTP Adaptive Streaming
Famaey, J., Latré, S., Bouten, N., de Meerssche, W. V., Vleeschauwer, B. D., Leekwijck, W. V., & Turck, F. D. (2013) · Ghent University Academic Bibliography (Ghent University) · Cited by 67 · see citing works
Self-citationDesign of a Q-learning-based client quality selection algorithm for HTTP adaptive video streaming
Claeys, M., Latré, S., Famaey, J., Wu, T., Leekwijck, W. V., & Turck, F. D. (2013) · Ghent University Academic Bibliography (Ghent University) · Cited by 63 · see citing works
Self-citationIntelligent control for adaptive video streaming
Menkovski, V., & Liotta, A. (2013) · International Conference on Consumer Electronics · doi:10.1109/icce.2013.6486825 · Cited by 43 · see citing works
QoE optimization through in-network quality adaptation for HTTP adaptive streaming
(2012) · Ghent University Academic Bibliography (Ghent University) · doi:10.5555/2499406.2499460 · Cited by 36 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning). MIT Press, Mar. 1998.
Cited by
Sorted by citations
Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018) · Journal of Internet Services and Applications · doi:10.1186/s13174-018-0087-2 · Cited by 1,036 · see citing works
Bian, K., & Priyadarshi, R. (2024) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-024-10110-w · Cited by 139 · see citing works
Adaptive Bitrate Selection: A Survey
Sani, Y., Mauthe, A., & Edwards, C. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2725241 · Cited by 121 · see citing works
Network Resource Allocation System for QoE-Aware Delivery of Media Services in 5G Networks
Martin, A., Egana, J., Florez, J., Montalban, J., Olaizola, I. G., Quartulli, M., Viola, R., & Zorrilla, M. (2018) · IEEE Transactions on Broadcasting · doi:10.1109/tbc.2018.2828608 · Cited by 99 · see citing works
In-Network Machine Learning Using Programmable Network Devices: A Survey
Zheng, C., Hong, X., Ding, D., Vargaftik, S., Ben-Itzhak, Y., & Zilberman, N. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3344351 · Cited by 90 · see citing works
QoE-Driven Dynamic Adaptive Video Streaming Strategy With Future Information
Yu, L., Tillo, T., & Xiao, J. (2017) · IEEE Transactions on Broadcasting · doi:10.1109/tbc.2017.2687698 · Cited by 78 · see citing works
QoE-Based Low-Delay Live Streaming Using Throughput Predictions
Miller, K., Al-Tamimi, A.-K., & Adam Wolisz, A. (2016) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/2990505 · Cited by 75 · see citing works
Park: An Open Platform for Learning-Augmented Computer Systems
Mao, H., Negi, P., Narayan, A., Wang, H., Yang, J., Wang, H., Marcus, R., Addanki, R., Shirkoohi, M. K., He, S., et al. (2019) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 66 · see citing works
Reinforcement learning for personalization: A systematic literature review
den Hengst, F., Grua, E. M., el Hassouni, A., & Hoogendoorn, M. (2020) · Data Science · doi:10.3233/ds-200028 · Cited by 60 · see citing works
LBP: Robust Rate Adaptation Algorithm for SVC Video Streaming
Elgabli, A., Aggarwal, V., Hao, S., Qian, F., & Sen, S. B. (2018) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2018.2844123 · Cited by 56 · see citing works
QoE in video streaming over wireless networks: perspectives and research challenges
Su, G.-M., Su, X., Bai, Y., Wang, M., Vasilakos, A. V., & Wang, H. (2016) · Wireless Networks · doi:10.1007/s11276-015-1028-7 · Cited by 55 · see citing works
Seamless Multimedia Delivery Within a Heterogeneous Wireless Networks Environment: Are We There Yet?
Trestian, R., Comşa, I.-S., & Tüysüz, M. F. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2789722 · Cited by 54 · see citing works
Performance Evaluation of Video Streaming Using MPEG DASH, RTSP, and RTMP in Mobile Networks
Aloman, A., Ispas, A. I., Ciotîrnae, P., Sanchez‐Iborra, R., & Cano, M. (2015) · IFIP Wireless and Mobile Networking Conference (WMNC) · doi:10.1109/wmnc.2015.12 · Cited by 50 · see citing works
An HTTP/2 Push-Based Approach for Low-Latency Live Streaming with Super-Short Segments
van der Hooft, J., Petrangeli, S., Wauters, T., Huysegems, R., Bostoen, T., & Turck, F. D. (2017) · Journal of Network and Systems Management · doi:10.1007/s10922-017-9407-2 · Cited by 44 · see citing works
Bouten, N., Schmidt, R. D. O., Famaey, J., Latré, S., Pras, A., & De Turck, F. (2015) · Computer Networks · doi:10.1016/j.comnet.2015.02.007 · Cited by 36 · see citing works
Self-citationQoE-Oriented Rate Adaptation for DASH With Enhanced Deep Q-Learning
Liu, J., Tao, X., & Lü, J. (2018) · IEEE Access · doi:10.1109/access.2018.2889999 · Cited by 31 · see citing works
Scalable Virtual Network Video-Optimizer for Adaptive Real-Time Video Transmission in 5G Networks
Salvá-García, P., Calero, J. M. A., Wang, Q., Arevalillo‐Herráez, M., & Bernabé, J. B. (2020) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2020.2978975 · Cited by 31 · see citing works
Simulation Framework for HTTP-Based Adaptive Streaming Applications
Ott, H., Miller, K., & Adam Wolisz, A. (2017) · Workshop on ns-3 (WNS3) · doi:10.1145/3067665.3067675 · Cited by 30 · see citing works
Over-the-top (OTT) platforms: a review, synthesis and research directions
Khanna, P., Sehgal, R., Gupta, A. K., Dubey, A. M., & Srivastava, R. (2024) · Marketing Intelligence & Planning · doi:10.1108/mip-03-2023-0122 · Cited by 29 · see citing works
An automated model for the assessment of QoE of adaptive video streaming over wireless networks
Taha, M., Ali, A., Lloret, J., Gondim, P. R. D. L., & Cánovas, A. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-10934-9 · Cited by 29 · see citing works
SmartStreamer: Preference-Aware Multipath Video Streaming Over MPTCP
Elgabli, A., & Aggarwal, V. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2915355 · Cited by 27 · see citing works
Izima, O., de Fréin, R., & Malik, A. (2021) · Electronics · doi:10.3390/electronics10222851 · Cited by 28 · see citing works
Design, optimization and evaluation of a q-learning HTTP adaptive streaming client
Martín, V., Cabrera, J., & Garcia, N. N. (2016) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2016.7838090 · Cited by 27 · see citing works
A Video Bitrate Adaptation and Prediction Mechanism for HTTP Adaptive Streaming
Sobhani, A., Yassine, A., & Shirmohammadi, S. (2017) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3052822 · Cited by 25 · see citing works
QoE-Driven Adaptive K-Push for HTTP/2 Live Streaming
Xu, Z., Zhang, X., & Guo, Z. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2849015 · Cited by 25 · see citing works
ALVS: Adaptive Live Video Streaming using deep reinforcement learning
Ozcelik, I. M., & Ersoy, C. (2022) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2022.103451 · Cited by 25 · see citing works
MEC Proxy for Efficient Cache and Reliable Multi-CDN Video Distribution
Viola, R., Martín, Á., Zorrilla, M., & Montalbán, J. (2018) · IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB) · doi:10.1109/bmsb.2018.8436904 · Cited by 23 · see citing works
Optimal Network-Assisted Multiuser DASH Video Streaming
Özfatura, E., Erçetin, Ö., & İnaltekin, H. (2018) · IEEE Transactions on Broadcasting · doi:10.1109/tbc.2018.2823644 · Cited by 22 · see citing works
A Network-Layer QoE Model for YouTube Live in Wireless Networks
Jiménez, L. R., Solera-Delgado, M., & Toril, M. (2019) · IEEE Access · doi:10.1109/access.2019.2918433 · Cited by 21 · see citing works
MEC for Fair, Reliable and Efficient Media Streaming in Mobile Networks
Martín, Á., Viola, R., Zorrilla, M., Flórez, J., Angueira, P., & Montalbán, J. (2019) · IEEE Transactions on Broadcasting · doi:10.1109/tbc.2019.2954097 · Cited by 21 · see citing works
QoE-aware adaptive bitrate video streaming over mobile networks with caching proxy
Dong, K., He, J., & Song, W. (2015) · 2012 International Conference on Computing, Networking and Communications (ICNC) · doi:10.1109/iccnc.2015.7069438 · Cited by 19 · see citing works
Hop-by-hop adaptive video streaming in content centric network
Liu, Z., & Wei, Y. (2016) · International Conference on Communications · doi:10.1109/icc.2016.7511585 · Cited by 19 · see citing works
Dubin, R., Dvir, A. Z., Pele, O., Hadar, O., Katz, I., & Mashiach, O. (2016) · Multimedia Systems · doi:10.1007/s00530-016-0525-6 · Cited by 18 · see citing works
Low-Latency Live Streaming Over HTTP in Bandwidth-Limited Networks
Ozcelik, I. M., & Ersoy, C. (2020) · IEEE Communications Letters · doi:10.1109/lcomm.2020.3030887 · Cited by 17 · see citing works
Cognitive streaming on android devices
Vega, M. T., Mocanu, D. C., Barresi, R., Fortino, G., & Liotta, A. (2015) · IFIP/IEEE International Symposium on Integrated Network Management (IM) · doi:10.1109/inm.2015.7140488 · Cited by 16 · see citing works
QoE-Based Analysis of DASH Streaming Parameters Over Mobile Broadcast Networks
Lentisco, C. M., Bellido, L., Juan Carlos Cuéllar, Q., Pastor, E., & Herrera, J. L. A. (2017) · IEEE Access · doi:10.1109/access.2017.2755438 · Cited by 16 · see citing works
ABR prediction using supervised learning algorithms
Yousef, H., Feuvre, J. L., & Storelli, A. (2020) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp48831.2020.9287123 · Cited by 13 · see citing works
GiantClient: Video HotSpot for Multi-User Streaming
Elgabli, A., Felemban, M., & Aggarwal, V. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2870715 · Cited by 11 · see citing works
Adaptive Rate Control for Live streaming using SRT protocol
Viola, R., Martín, Á., Mogollón, F., Gabilondo, Á., Morgade, J., Zorrilla, M., Montalbán, J., & Angueira, P. (2020) · IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB) · doi:10.1109/bmsb49480.2020.9379708 · Cited by 10 · see citing works
Reinforcement Learning In DASH
Khan, K., & Goodridge, W. S. (2020) · International Journal of Advanced Networking and Applications · doi:10.35444/ijana.2020.11052 · Cited by 9 · see citing works
A quality of experience model for live video in first-person-view drone control in cellular networks
González, N., Solera-Delgado, M., Ruiz, F., Gijón, C., & Toril, M. (2023) · Computer Networks · doi:10.1016/j.comnet.2023.110089 · Cited by 9 · see citing works
QoE-based enhancements of Chunked CMAF over low latency video streams
Viola, R., Gabilondo, Á., Martín, Á., Mogollón, F., Zorrilla, M., & Montalbán, J. (2019) · IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB) · doi:10.1109/bmsb47279.2019.8971894 · Cited by 9 · see citing works
Audiovisual quality of live music streaming over mobile networks using MPEG-DASH
Rodrigues, R., Počta, P., Melvin, H., Bernardo, M. V., Pereira, M., & Pinheiro, A. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09047-6 · Cited by 9 · see citing works
An HTTP/2 push-based approach for SVC adaptive streaming
van der Hooft, J., Petrangeli, S., Bouten, N., Wauters, T., Huysegems, R., Bostoen, T., & Turck, F. D. (2016) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms.2016.7502802 · Cited by 9 · see citing works
Smoothness Constraint Based Stochastic Optimization for Wireless Scalable Video Streaming
Si, P., Yang, J., Chen, S., & Xi, H. (2015) · IEEE Communications Letters · doi:10.1109/lcomm.2015.2415784 · Cited by 8 · see citing works
Bitrate adaptation for seamless on-demand video streaming over mobile networks
Le, H. T., Ngoc, N. P., & Truong, C.-T. (2018) · Signal Processing Image Communication · doi:10.1016/j.image.2018.03.014 · Cited by 8 · see citing works
QoE-Aware Resource Allocation for Small Cells
Elgabli, A., Elghariani, A., Aggarwal, V., & Bell, M. R. (2018) · Globecom · doi:10.1109/glocom.2018.8647828 · Cited by 8 · see citing works
Optimizing Immersive Video Coding Configurations Using Deep Learning: A Case Study on TMIV
Hsu, C.-F., Hung, T.-H., & Hsu, C.-H. (2022) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3471191 · Cited by 8 · see citing works
The Upstream Matters: Impact of Uplink Performance on YouTube 360° Live Video Streaming in LTE
Jiménez, L. R., Solera-Delgado, M., Toril, M., Luna-Ramírez, S., & Bejarano-Luque, J. L. (2021) · IEEE Access · doi:10.1109/access.2021.3110284 · Cited by 8 · see citing works
A reinforcement learning approach for QoS/QoE model identification
Canale, S., Priscoli, F. D., Monaco, S., Palagi, L., & Suraci, V. (2015) · Chinese Control Conference (CCC) · doi:10.1109/chicc.2015.7259941 · Cited by 7 · see citing works
KNN-Q Learning Algorithm of Bitrate Adaptation for Video Streaming over HTTP
Lin, H., Shen, Z., Zhou, H., Liu, X., Zhang, L., Xiao, G., & Cheng, Z. (2020) · Information Communication Technologies Conference (ICTC) · doi:10.1109/ictc49638.2020.9123312 · Cited by 7 · see citing works
D2D-assisted user-centric adaptive video transmission in next generation cellular networks
Barik, P. K., Singhal, C., & Datta, R. (2022) · Physical Communication · doi:10.1016/j.phycom.2022.101944 · Cited by 7 · see citing works
Nguyen, H. H. C., Grzonka, D., Khoa, B. T., Sagar, K. V. D., Abbasi, I. A., Mahaveerakannan, R., & Alkhayyat, A. H. (2023) · Mobile Networks and Applications · doi:10.1007/s11036-023-02214-7 · Cited by 7 · see citing works
Use of Machine Learning for Rate Adaptation in MPEG-DASH for Quality of Experience Improvement
Alzahrani, I. R., Ramzan, N., Katsigiannis, S., & Amira, A. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-78753-4_1 · Cited by 7 · see citing works
Chunk Duration--Aware SDN-Assisted DASH
Ozcelik, I. M., & Ersoy, C. (2019) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3337681 · Cited by 7 · see citing works
Dynamic server selection strategy for multi-server HTTP adaptive streaming services
Bouten, N., Claeys, M., Van Poecke, B., Latre, S., & De Turck, F. (2016) · 2016 12th International Conference on Network and Service Management (CNSM) · doi:10.1109/cnsm.2016.7818403 · Cited by 6 · see citing works
Self-citationShahab, M. B., Usman, M. A., & Shin, S. Y. (2016) · IEEE Communications Letters · doi:10.1109/lcomm.2016.2620979 · Cited by 6 · see citing works
Optimized Preference-Aware Multi-Path Video Streaming with Scalable Video Coding
Elgabli, A., Liu, K., & Aggarwal, V. (2018) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2018.2889039 · Cited by 6 · see citing works
WBQoEMS: Web browsing QoE monitoring system based on prediction algorithms
Letaïfa, A. B. (2019) · International Journal of Communication Systems · doi:10.1002/dac.4007 · Cited by 6 · see citing works
Dynamic Server Selection Strategy for Multi-server HTTP Adaptive Streaming Services
Bouten, N., Claeys, M., Poecke, B. V., Latré, S., & Turck, F. D. (2016) · Conference on Network and Service Management · doi:10.5555/3375069.3375079 · Cited by 5 · see citing works
Self-citationQoE Issues of OTT Services over 5G Network
Feng-Hui, H., Wen-An, Z., & Yu, D. (2014) · International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA) · doi:10.1109/bwcca.2014.74 · Cited by 5 · see citing works
QoE-fair Resource Allocation for DASH Video Delivery Systems
Cicco, L. D., Manfredi, G., Mascolo, S., & Palmisano, V. (2019) · doi:10.1145/3347447.3356753 · Cited by 5 · see citing works
An Improved DDPG Reinforcement Learning Control of Underwater Gliders for Energy Optimization
Jing, A., Tang, Z., Gao, J. S., & Pan, G. (2020) · IEEE International Conference on Unmanned Systems (ICUS) · doi:10.1109/icus50048.2020.9274883 · Cited by 5 · see citing works
A novel dynamic adaptive video streaming solution in content-centric mobile network
Wei, Y., Xu, C., Wang, M., & Guan, J. (2016) · IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) · doi:10.1109/pimrc.2016.7794977 · Cited by 5 · see citing works
GroupCast: Preference-Aware Cooperative Video Streaming With Scalable Video Coding
Elgabli, A., Felemban, M., & Aggarwal, V. (2018) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infcomw.2018.8406852 · Cited by 4 · see citing works
Lingareddy, V., & Damle, M. (2022) · International Interdisciplinary Humanitarian Conference for Sustainability (IIHC) · doi:10.1109/iihc55949.2022.10060746 · Cited by 4 · see citing works
Improved Video QoE in Wireless Networks Using Deep Reinforcement Learning
Moura, H. D., Oliveira, J. M., Soares, D. J. F., Macedo, D. F., & Vieira, M. A. M. (2023) · International Conference on Network and Service Management (CNSM) · doi:10.23919/cnsm59352.2023.10327822 · Cited by 4 · see citing works
Reinforcement Learning-Based Resource Allocation for Streaming in a Multi-Modal Deep Space Network
Ha, T., Oh, J., Lee, D., Lee, J., Jeon, Y., & Cho, S. (2021) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc52510.2021.9621165 · Cited by 4 · see citing works
Clustering‐based quality selection heuristics for HTTP adaptive streaming over cache networks
van der Hooft, J., Bouten, N., Vleeschauwer, D. D., Leekwijck, W. V., Wauters, T., Latré, S., & Turck, F. D. (2018) · International Journal of Network Management · doi:10.1002/nem.2046 · Cited by 3 · see citing works
Martín, Á., Viola, R., Gorostegui, J., Zorrilla, M., Flórez, J., & Montalbán, J. (2017) · Journal of Real-Time Image Processing · doi:10.1007/s11554-017-0728-x · Cited by 3 · see citing works
Tran, C. M., Duc, T. N., Bach, N. G., Tan, P. X., & Kamioka, E. (2022) · Electronics · doi:10.3390/electronics11131949 · Cited by 3 · see citing works
Generative AI for HTTP Adaptive Streaming
Artioli, E. (2024) · ACM Multimedia Systems Conference (MMSys) · doi:10.1145/3625468.3652912 · Cited by 3 · see citing works
Kumar, D., Easwaran, N. K., Srinivasan, A., Shankar, A., & Raj, L. A. (2015) · ITU Kaleidoscope Academic Conference (ITU K) · doi:10.1109/kaleidoscope.2015.7383639 · Cited by 3 · see citing works
QABR: A QoE-Based Approach to Adaptive Bitrate Selection in Video Streaming Services
Tran, C. M. (2019) · International Journal of Advanced Trends in Computer Science and Engineering · doi:10.30534/ijatcse/2019/2181.42019 · Cited by 3 · see citing works
A Failsoft Scheme for Mobile Live Streaming by Scalable Video Coding
OKADA, H., Yoshimi, M., Wu, C., & Yoshinaga, T. (2021) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2021pap0015 · Cited by 3 · see citing works
A Novel Dynamic Bit Rate Analysis Technique for Adaptive Video Streaming over HTTP Support
Kumar, P. M. A., Raj, L. A., Jyothi, B., Soliman, N. F., Bajaj, M., & El‐Shafai, W. (2022) · Sensors · doi:10.3390/s22239307 · Cited by 3 · see citing works
FastScan: Robust Low-Complexity Rate Adaptation Algorithm for Video Streaming over HTTP
Elgabli, A., & Aggarwal, V. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2914388 · Cited by 3 · see citing works
Reinforcement learning - based adaptation and scheduling methods for multi-source DASH
Nguyen, N., Luu, L., Vo, P. L., Nguyen, S., Do, C., & Nguyên, N. T. (2022) · Computer Science and Information Systems · doi:10.2298/csis220927055n · Cited by 2 · see citing works
A Quality Selection Mechanism Using a Deep Q-Network for Seamless Video Streaming Services
Kim, I., Hong, S.-J., Jung, S., & Lim, K. (2018) · International Conference on Ubiquitous and Future Networks · doi:10.1109/icufn.2018.8437024 · Cited by 2 · see citing works
VPAP: VBR Pattern Aware Playback Buffering for video streaming
Ariyasinghe, L., Huang, Z., Zhang, H., & Eyers, D. M. (2016) · International Telecommunication Networks and Applications Conference (ITNAC) · doi:10.1109/atnac.2016.7878782 · Cited by 2 · see citing works
Quality Enhanced Multimedia Content Delivery for Mobile Cloud with Deep Reinforcement Learning
Saleem, M., Saleem, Y., Asif, H. M. S., & Mian, M. S. (2019) · Wireless Communications and Mobile Computing · doi:10.1155/2019/5038758 · Cited by 2 · see citing works
5G Development: Automation and the Role of Artificial Intelligence
Kilinc, C., Sun, C., & Marina, M. K. (2020) · Wiley 5G Ref · doi:10.1002/9781119471509.w5gref128 · Cited by 2 · see citing works
Deadline‐aware TCP congestion control for HTTP adaptive streaming services
Claeys, M., Bouten, N., Vleeschauwer, D. D., Schepper, K. D., Leekwijck, W. V., Latré, S., & Turck, F. D. (2018) · International Journal of Network Management · doi:10.1002/nem.2023 · Cited by 1 · see citing works
Self-citationDeep reinforcement learning enhanced optimization algorithm for adaptive bitrate video streaming
Zhang, J., Han, Y., Cai, Z., Feng, Y., & Zhu, L. (2025) · AIP Advances · doi:10.1063/5.0277381 · Cited by 2 · see citing works
HTTP adaptive streaming scheme for improving the quality of experience in multi-server environments
Yun, D., Park, W., Kim, S.-J., & Chung, K. (2018) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2018.01.003 · Cited by 1 · see citing works
Collaborative Methods to Reduce the Disastrous Effects of the Overlapping ON Problem in DASH
Khan, K., & Goodridge, W. S. (2019) · International Journal of Advanced Networking and Applications · doi:10.35444/ijana.2019.11028 · Cited by 1 · see citing works
Advanced Method for QoE Evaluation and Improvement in Modern Cellular Networks
Одарченко, Р. С., Abakumova, A., Bondar, S. V., Bogachuk, Y., & Maratkyzy, K. (2020) · International Scientific-Practical Conference Problems of Infocommunications Science and Technology · doi:10.1109/picst51311.2020.9468036 · Cited by 1 · see citing works
Internet Multimedia Content Distribution
Li, Z.-N., Drew, M. S., & Liu, J. (2014) · Texts in computer science · doi:10.1007/978-3-319-05290-8_16 · Cited by 1 · see citing works
Adaptive Driving-Speed Control at Signalised Intersection Using Reinforcement Learning
Gamage, H. D. D. (2018) · Queensland University of Technology · doi:10.5204/thesis.eprints.121732 · Cited by 1 · see citing works
Research on streaming video information service profit model based on the value chain
Hou, D., & Chen, J. (2015) · International Conference on Computer Science and Network Technology (ICCSNT) · doi:10.1109/iccsnt.2015.7490763
Designing Efficient Mobile Gateway for Impoverished Regions Based on Self-Learning Strategy
Zhou, Y., Shi, W., Song, F., & You, I. (2016) · Mobile Information Systems · doi:10.1155/2016/4718474
Layer approach for HTTP-based low-delay adaptive streaming in mobile networks
Ngoc, H. L., Miller, K., Behboodi, A., & Adam Wolisz, A. (2017) · IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) · doi:10.1109/wowmom.2017.7974322
Towards QoE-aware HAS video streaming over LTE
Sobhani, A., Yassine, A., & Shirmohammadi, S. (2017) · IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) · doi:10.1109/pimrc.2017.8292778
Research on quality metrics of wireless adaptive video streaming
Li, X. (2018) · International Conference on Graphics and Image Processing (ICGIP) · doi:10.1117/12.2302493
A User Gaze Detection Based Approach to Squeeze Idle Video Traffic in Communication Networks
Shahab, M. B., & Shin, S. Y. (2018) · International Conference on Computer and Communication Engineering · doi:10.1109/iccce.2018.8539248
Ouyang, Y., Su, L., Lu, W., Lin, Z.-Y., Shah, M., & Li, Z. (2019) · Wireless Telecommunications Symposium · doi:10.1109/wts.2019.8715537
A QoE Fairness Approach for Adaptive Video Streaming
Fisher, D. G. (2019) · doi:10.11575/prism/37351
Song, W. (2018) · Springer eBooks · doi:10.1007/978-3-319-32903-1_60-1
Hybrid Multicast and Unicast Streaming System with Layered Video Coding : (Extended Abstract)
Tsai, M.-C., & Yang, S. (2021) · Wireless and Optical Communications Conference · doi:10.1109/wocc53213.2021.9602985
- Authors
- Claeys, M., Latre, S., Famaey, J., & De Turck, F.
- Affiliations
- Ghent University (BE)
- Venue
- IEEE Communications Letters
- Published
- 2014-04
- Volume
- 18
- Issue
- 4
- Pages
- 716-719
- ISSN
- 1089-7798 (ISSN-L)
- OpenAlex
- W1998431331
- Cited by
- 111
- References
- 10
- Citations by year
- 2025: 3 · 2024: 3 · 2023: 6 · 2022: 9 · 2021: 8 · 2020: 13 · 2019: 15 · 2018: 22 · 2017: 10 · 2016: 11 · 2015: 9 · 2014: 2
- FWCI
- 9.54
- Type
- journal article
- Language
- en
- Open access
- green
- biblio.ugent.be
- Locations
- IEEE Communications Letters (publishedVersion)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion, other-oa)
- Funders
- European Commission (318488)
- Vlaamse regering
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Physical Sciences › Computer Science › Computer Networks and Communications › Caching and Content Delivery
- Keywords
- reinforcement learning, quality of experience, cellular networks, video segmentation
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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