, Volume 101, Issue 5, pp 435–453 | Cite as

Cooperative caching for HTTP-based adaptive streaming contents in cache-enabled radio access networks

  • Phuong L. VoEmail author
  • Nguyen H. Tran


The rapid growth of the number of mobile broadband subscribers has led to an exponential increase of the Internet traffic, of which a major part is video traffic. In 4G/5G mobile networks, the eNBs should cache the popular contents to reduce the transit cost at the backhaul links. Recently, HTTP-based adaptive streaming is widely used to transfer the video contents to the end users in the Internet. With HTTP-based adaptive streaming, each video content is stored in several representations corresponding to different video performances. The representation with a better performance yields a higher user satisfaction, however, it consumes more storage and transit cost. In this paper, we propose a cooperative caching model in which the eNBs collaborate in caching and request routing the video contents. The proposed optimization problem is a large-scale integer linear program which is an NP-hard problem. Based on alternating direction method of multipliers technique, a distributed algorithm converging to the solution to the relaxation problem is then developed. It results to fractional caching and request routing which serve as a performance benchmark of the cooperative caching. Moreover, we propose a nearest-neighbor request routing policy and a lightweight-cooperative eviction algorithm for integral caching. Extensive simulations show that the proposed request routing and eviction algorithms achieve more than 78% of the performance benchmark.


Cooperative caching Adaptive bitrate streaming Radio access network Alternating direction method of multipliers 

Mathematics Subject Classification

90B18 68M99 



This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under Grant Number 102.02-2015.36.


  1. 1.
    ITU, ICT Facts and Figures 2016 (2016)Google Scholar
  2. 2.
    Wang X, Chen M, Taleb T, Ksentini A, Leung V (2014) Cache in the air: exploiting content caching and delivery techniques for 5G systems. IEEE Commun Mag 52(2):131–139CrossRefGoogle Scholar
  3. 3.
    Andreev S, Galinina O, Pyattaev A, Hosek J, Masek P, Yanikomeroglu H, Koucheryavy Y (2016) Exploring synergy between communications, caching, and computing in 5G-grade deployments. IEEE Commun Mag 54(8):60–69CrossRefGoogle Scholar
  4. 4.
    Bastug E, Bennis M, Debbah M (2014) Living on the edge: The role of proactive caching in 5G wireless networks. IEEE Commun Mag 52(8):82–89CrossRefGoogle Scholar
  5. 5.
    Xu Y, Li Y, Wang Z, Lin T, Zhang G, Ci S (2014) Coordinated caching model for minimizing energy consumption in radio access network. In: IEEE international conference on communications (ICC), pp 2406–2411Google Scholar
  6. 6.
    Gharaibeh A, Khreishah A, Ji B, Ayyash M (2016) A provably efficient online collaborative caching algorithm for multicell-coordinated systems. IEEE Trans Mob Comput 15(8):1863–1876CrossRefGoogle Scholar
  7. 7.
    Jacobson V, Smetters DK, Thornton JD, Plass MF, Briggs NH, Braynard RL (2009) Networking named content. In: The 5th international conference on Emerging networking experiments and technologies, pp 1–12Google Scholar
  8. 8.
    Raghavan RMP (1995) Randomized algorithms. Cambridge University Press, New YorkzbMATHGoogle Scholar
  9. 9.
    Cao P, Irani S (1997) Cost-aware www proxy caching algorithms. In Usenix symposium on internet technologies and systems, vol 12, no 97, pp 193–206Google Scholar
  10. 10.
    Psounis K, Prabhakar B (2002) Efficient randomized web-cache replacement schemes using samples from past eviction times. IEEE/ACM Trans Netw (TON) 10(4):441–455CrossRefGoogle Scholar
  11. 11.
    Borst S, Gupta V, Walid A (2010) Distributed caching algorithms for content distribution networks. In: IEEE INFOCOM, pp 1–9Google Scholar
  12. 12.
    Applegate D, Archer A, Gopalakrishnan V, Lee S, Ramakrishnan KK (2016) Optimal content placement for a large-scale VoD system. IEEE/ACM Trans Netw 24(4):2114–2127CrossRefGoogle Scholar
  13. 13.
    Dai J, Hu Z, Li B, Liu J, Li B (2012) Collaborative hierarchical caching with dynamic request routing for massive content distribution. In: IEEE INFOCOM, pp 2444–2452Google Scholar
  14. 14.
    Li X, Wang X, Li K, Han Z, Leung VC (2017) Collaborative multi-tier caching in heterogeneous networks: modeling, analysis, and design. IEEE Trans Wireless Commun 16(10):6926–6939CrossRefGoogle Scholar
  15. 15.
    Tanzil SS, Hoiles W, Krishnamurthy V (2017) Adaptive scheme for caching youtube content in a cellular network: machine learning approach. IEEE Access 5:5870–5881CrossRefGoogle Scholar
  16. 16.
    Cisco (2016) Cisco visual networking index: Forecast and methodology, 2015-2020Google Scholar
  17. 17.
    Stockhammer T (2011) Dynamic adaptive streaming over HTTP: standards and design principles. In: The 2nd annual ACM conference on Multimedia systems, pp 133–144Google Scholar
  18. 18.
    Oyman O, Singh S (2012) Quality of experience for HTTP adaptive streaming services. IEEE Commun Mag 5(4):20–27CrossRefGoogle Scholar
  19. 19.
    Apple, Using http live streaming. [Online]. Accessed 1 Apr 2018
  20. 20.
  21. 21.
    Youtube, ‘Recommended upload encoding settings (Advanced). [Online]. Accessed 1 Apr 2018
  22. 22.
    Zhang W, Wen Y, Chen Z, Khisti A (2013) Qoe-driven cache management for http adaptive bit rate streaming over wireless networks. IEEE Trans Multimed 15(6):1431–1445CrossRefGoogle Scholar
  23. 23.
    Reichl P, Tuffin B, Schatz R (2013) Logarithmic laws in service quality perception: where microeconomics meets psychophysics and quality of experience. Telecommun Syst 52(2):587–600Google Scholar
  24. 24.
    Egger S, Reichl P, Hofeld T, Schatz R (2012) “Time is bandwidth”? Narrowing the gap between subjective time perception and Quality of Experience. In: IEEE ICC, pp 1325–1330Google Scholar
  25. 25.
    Kelly FP, Maulloo AK, Tan DK (1998) Rate control for communication networks: shadow prices, proportional fairness and stability. J Oper Res Soc 49(3):237–252CrossRefzbMATHGoogle Scholar
  26. 26.
    Boyd S, Parikh N, Chu E, Peleato B, Eckstein J (2011) Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends Mach Learn 3(1):1–122CrossRefzbMATHGoogle Scholar
  27. 27.
    Kellerer H, Pferschy U, Pisinger D (2004) Knapsack Problems. Springer, Berlin, HeidelbergCrossRefzbMATHGoogle Scholar
  28. 28.
    Breslau L, Cao P, Fan L, Phillips G, Shenker S (1999) Web caching and zipf-like distributions: evidence and implications. In: IEEE INFOCOM, pp 126–134Google Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringInternational University - VNUHCMHo Chi MinhVietnam
  2. 2.School of Information TechnologiesThe University of SydneySydneyAustralia

Personalised recommendations