Video caching and scheduling with edge cooperation

Zhidu Li , Fuxiang Li , Tong Tang , Hong Zhang , Jin Yang

›› 2024, Vol. 10 ›› Issue (2) : 450 -460.

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›› 2024, Vol. 10 ›› Issue (2) :450 -460. DOI: 10.1016/j.dcan.2022.09.012
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Video caching and scheduling with edge cooperation

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Abstract

In this paper, we explore a distributed collaborative caching and computing model to support the distribution of adaptive bit rate video streaming. The aim is to reduce the average initial buffer delay and improve the quality of user experience. Considering the difference between global and local video popularities and the time-varying characteristics of video popularity, a two-stage caching scheme is proposed to push popular videos closer to users and minimize the average initial buffer delay. Based on both long-term content popularity and short-term content popularity, the proposed caching solution is decouple into the proactive cache stage and the cache update stage. In the proactive cache stage, we develop a proactive cache placement algorithm that can be executed in an off-peak period. In the cache update stage, we propose a reactive cache update algorithm to update the existing cache policy to minimize the buffer delay. Simulation results verify that the proposed caching algorithms can reduce the initial buffer delay efficiently.

Keywords

Video service / Distributed and collaborative caching / Long-term popularity / Short-term popularity

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Zhidu Li, Fuxiang Li, Tong Tang, Hong Zhang, Jin Yang. Video caching and scheduling with edge cooperation. , 2024, 10(2): 450-460 DOI:10.1016/j.dcan.2022.09.012

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References

[1]

X. Zhang, T. Lv, Y. Ren, W. Ni, N.C. Beaulieu, Y.J. Guo, Economical caching for scalable videos in cache-enabled heterogeneous networks, IEEE J. Sel. Area. Commun. 37 (7) (2019) 1608-1621.

[2]

B. Jedari, G. Premsankar, G. Illahi, M.D. Francesco, A. Mehrabi, A. Ylä-Jääski, Video caching, analytics, and delivery at the wireless edge: a survey and future directions, IEEE Communications Surveys Tutorials 23 (1) (2021) 431-471.

[3]

B. Fan, Y. Wu, Z. He, Y. Chen, T.Q. Quek, C.-Z. Xu, Digital twin empowered mobile edge computing for intelligent vehicular lane-changing, IEEE Network 35 (6) (2021) 194-201.

[4]

Y. Li, H. Ma, L. Wang, S. Mao, G. Wang, Optimized content caching and user association for edge computing in densely deployed heterogeneous networks, IEEE Trans. Mobile Comput. 21 (6) (2020) 2130-2142.

[5]

C. Li, L. Toni, J. Zou, H. Xiong, P. Frossard, Qoe-driven mobile edge caching placement for adaptive video streaming, IEEE Trans. Multimed. 20 (4) (2018) 965-984.

[6]

M. Cha, H. Kwak, P. Rodriguez, Y.-Y. Ahn, S. Moon, I tube you tube, everybody tubes: analyzing the world's largest user generated content video system,in:Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, IMC ’07, Association for Computing Machinery, New York, NY, USA, 2007, pp. 1-14.

[7]

S. Krishnan, M. Afshang, H.S. Dhillon, Effect of retransmissions on optimal caching in cache-enabled small cell networks, IEEE Trans. Veh. Technol. 66 (12) (2017) 11383-11387.

[8]

Z. Chang, Y. Hu, Y. Chen, B. Zeng, Cluster-oriented device-to-device multimedia communications: joint power, bandwidth, and link selection optimization, IEEE Trans. Veh. Technol. 67 (2) (2018) 1570-1581.

[9]

T. Stockhammer, Dynamic Adaptive Streaming over HTTP-:Standards and Design Principles, Association for Computing Machinery, New York, NY, USA, 2011, pp. 133-144.

[10]

D. Wu, R. Bao, Z. Li, H. Wang, H. Zhang, R. Wang, Edge-cloud collaboration enabled video service enhancement: a hybrid human-artificial intelligence scheme, IEEE Trans. Multimed. 23 (2021) 2208-2221.

[11]

Z. Li, X. Gao, Q. Li, J. Guo, B. Yang, Edge caching enhancement for industrial internet: a recommendation-aided approach, IEEE Internet Things J. 9 (18) (2022) 16941-16952.

[12]

Q. Chen, W. Wang, F.R. Yu, M. Tao, Z. Zhang, Content caching oriented popularity prediction: a weighted clustering approach, IEEE Trans. Wireless Commun. 20 (1)(2021) 623-636.

[13]

J. Liang, D. Zhu, H. Liu, H. Ping, T. Li, H. Zhang, L. Geng, Y. Liu, Multi-head attention based popularity prediction caching in social content-centric networking with mobile edge computing, IEEE Commun. Lett. 25 (2) (2021) 508-512.

[14]

T.X. Tran, A. Hajisami, P. Pandey, D. Pompili, Collaborative mobile edge computing in 5g networks: new paradigms, scenarios, and challenges, IEEE Commun. Mag. 55 (4) (2017) 54-61.

[15]

A. Zhang, Q. Li, Y. Chen, X. Ma, L. Zou, Y. Jiang, Z. Xu, G.-M. Muntean, Video super-resolution and caching-an edge-assisted adaptive video streaming solution, IEEE Trans. Broadcast. 67 (4) (2021) 799-812.

[16]

C. Qiao, J. Wang, Y. Liu, Beyond QoE: diversity adaptation in video streaming at the edge, IEEE/ACM Trans. Netw. 29 (1) (2021) 289-302.

[17]

T.X. Tran, D. Pompili, Adaptive bitrate video caching and processing in mobile-edge computing networks, IEEE Trans. Mobile Comput. 18 (9) (2019) 1965-1978.

[18]

S. Xia, Z. Yao, Y. Li, S. Mao, Online distributed offloading and computing resource management with energy harvesting for heterogeneous mec-enabled iot, IEEE Trans. Wireless Commun. 20 (10) (2021) 6743-6757.

[19]

Y. Han, L. Ai, R. Wang, J. Wu, D. Liu, H. Ren, Cache placement optimization in mobile edge computing networks with unaware environment-an extended multi-armed bandit approach, IEEE Trans. Wireless Commun. 20 (12) (2021) 8119-8133.

[20]

Y. Li, Z. Chen, M. Tao, Coded caching with device computing in mobile edge computing systems, IEEE Trans. Wireless Commun. 20 (12) (2021) 7932-7946.

[21]

H. Feng, S. Guo, L. Yang, Y. Yang, Collaborative data caching and computation offloading for multi-service mobile edge computing, IEEE Trans. Veh. Technol. 70 (9) (2021) 9408-9422.

[22]

G. Qiao, S. Leng, S. Maharjan, Y. Zhang, N. Ansari, Deep reinforcement learning for cooperative content caching in vehicular edge computing and networks, IEEE Internet Things J. 7 (1) (2020) 247-257.

[23]

H. Ahlehagh, S. Dey, Adaptive bit rate capable video caching and scheduling, in: 2013 IEEE Wireless Communications and Networking Conference, WCNC, 2013, pp. 1357-1362.

[24]

H.A. Pedersen, S. Dey, Enhancing mobile video capacity and quality using rate adaptation, ran caching and processing, IEEE/ACM Trans. Netw. 24 (2) (2016) 996-1010.

[25]

Z. Tan, Y. Wang, Y. Zhang, J. Zhou, A novel time series approach for predicting the long-term popularity of online videos, IEEE Trans. Broadcast. 62 (2) (2016) 436-445.

[26]

H. Zhao, Q. Zheng, W. Zhang, B. Du, H. Li, A segment-based storage and transcoding trade-off strategy for multi-version vod systems in the cloud, IEEE Trans. Multimed. 19 (1) (2017) 149-159.

[27]

X. Li, X. Wang, S. Xiao, V.C.M. Leung, Delay performance analysis of cooperative cell caching in future mobile networks, in: 2015 IEEE International Conference on Communications, ICC, 2015, pp. 5652-5657.

[28]

X. Huang, Z. Zhao, H. Zhang, Cooperate caching with multicast for mobile edge computing in 5g networks, in: 2017 IEEE 85th Vehicular Technology Conference, VTC Spring, 2017, pp. 1-6.

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