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Intelligent Design of Future mobile Internet for Enhanced Experience


  • Fen Zhou, LIA, Univesity of Avignon
  • Rachid El-Azouzi, LIA, Univesity of Avignon
  • Majed HADDAD, LIA, Univesity of Avignon

Mobile networks are rapidly evolving towards new technologies that are characterized by an increasingly sophisticated radio interface, always with the aim of higher bit rates and ubiquitous coverage. Although 4G network deployments are still in their beginnings, the first upgrades towards LTE-A solutions are already planned by operators and evolutions towards 5G networks are currently under research all over the world.

With the introduction of 4th generation mobile networks, applications such as streaming video for mobile devices become possible. However, the rise in popularity of these services is creating a huge bottleneck in the air interface between base stations and mobile devices. The main challenge of this work is to bridge between QoS and QoE in order to allow users to be satisfied as much as possible with respect to their actual QoE expectations. The classical methodologies to characterize the relationship between QoE and QoS have been designed originally for TV broadcasting services. But a number of studies have showed that this relationship for online video streaming becomes more complex and depends on the applications (e.g., browser, devices) and the container (e.g., Flash, HTML5, Sliverlight). The first objective of this internship is to understand the relationship between user perception and QoS metrics. In particular, it is of interest to gain insight into the principle ways in which QoS parameters affect the quantitative parts of QoE under the “real world” scenarios.

Another problem will be studied in this internship, is related to the network congestion in wireless networks. Indeed, the rate available to users in a wireless network is highly unpredictable and varies dramatically over time and space due to channel and interference fluctuations. However there is clearly a need to dynamically adapt the application parameters as function of the network condition in order resolve a central dilemma: the delivery of applications to the end user at maximum QoE with minimum cost. Thus, scalable dynamic QoE for a specific application requires an information exchange between application and network. The goal of this part is to study how a network can optimize the QoE of such applications and to understand which types of information are needed in order to recognize or predict when a QoE degradation occurs. For example, this information can include the network environment (e.g., technologies: 3G or 4G), the network condition (e.g., available bandwidth, number of users, mobility pattern) and application specific information (e.g., video bitrate, encoding, content genre). Some cross-layer optimization and learning algorithms at user level will be developed in order to deliver optimal QoE so that the user may not get dissatisfied or abandon the service. One promising solution is to integrate scalable video coding, radio broadcast and ad-hoc forwarding, as proposed in paper [1]. A mobile user may improve further his video quality via receiving the layers forwarded by a mobile user nearer to the base station.

The objective of this internship is to study the following problems: (i) How to efficiently allocate radio resources for different layers to maximize video quality among all mobile users? (ii) Design efficient routing protocol to maximize the gain of ad-hoc video forwarding (iii) LTE is based on OFDM, so it is possible to distribute available resource blocks (transmission radio resources in frequency domain) to different mobile users. Design efficient scheduler to optimize video quality while guaranteeing the quality of service (the end-to-end delay). Generally, these problems can be formulated as combinatorial optimization problems. We also plan to use some existing stochastic learning algorithms in which at each iteration, the user level uses the information sent by the network to update its strategies (e.g., buffering or coding rate..) in order to converge to a optimal policy that maximizes the QoE. Moreover, the complexity of the proposed solution should also be valuated in order to prove the efficiency of the stochastic model.



  • [1] Sha Hua, Yang Guo, Yong Liu, Hang Liu, Shivendra S. Panwar. Scalable Video Multicast in Hybrid 3G/Ad-Hoc Networks. IEEE Transaction on Multimedia, vol. 13, no. 2, pp. 402-413, 2011.
  • [2] F Zhou, J Liu, G Simon, R Boutaba. Joint optimization for the delivery of multiple video channels in Telco-CDN. In Proceeding of IEEE/ACM CNSM, pp1-5, 2013.
  • [3] Y Xu, E Altman, R El-Azouzi, M Haddad, S Elayoubi, T Jimenez, "Probabilistic analysis of buffer starvation in markovian queues", INFOCOM, 2012 Proceedings IEEE, 1826-1834, 2012