Mobility-Aware Streaming Rate Recommendation System

In mobile multimedia streaming services, important requirements consist of the support of service continuity, the guarantee of acceptable Quality of Service (QoS) and insurance of steady Quality of Experience (QoE). How to get a uniform data exchange rate during the entire (or partial) course of a streaming service while a user is on the move is an important challenge. Generally speaking, the streaming rate of a multimedia service may heavily fluctuate due to the unavailability or deficiency of resources along the movement path of a user. To cope with this challenge, this paper proposes a framework that integrates user mobility prediction models with resource availability prediction models to keep a constant or less fluctuating streaming rate and to ultimately ensure steady QoE. Simulations are conducted to evaluate the performance of the proposed framework in achieving its design objectives and encouraging results are obtained.

[1]  Pratap S. Prasad,et al.  Movement Prediction in Wireless Networks Using Mobility Traces , 2010, 2010 7th IEEE Consumer Communications and Networking Conference.

[2]  Tarik Taleb,et al.  A Cross-Layer Approach for an Efficient Delivery of TCP/RTP-Based Multimedia Applications in Heterogeneous Wireless Networks , 2008, IEEE Transactions on Vehicular Technology.

[3]  Sherif Akoush,et al.  Movement Prediction Using Bayesian Learning for Neural Networks , 2007, 2007 Second International Conference on Systems and Networks Communications (ICSNC 2007).

[4]  Ahmed Karmouch,et al.  A mobility prediction architecture based on contextual knowledge and spatial conceptual maps , 2005, IEEE Transactions on Mobile Computing.

[5]  Tracy Camp,et al.  A survey of mobility models for ad hoc network research , 2002, Wirel. Commun. Mob. Comput..

[6]  Cecilia Mascolo,et al.  A community based mobility model for ad hoc network research , 2006, REALMAN '06.

[7]  Yue Wang,et al.  Mobility Prediction in Cellular Network Using Hidden Markov Model , 2010, 2010 7th IEEE Consumer Communications and Networking Conference.

[8]  Cecilia Mascolo,et al.  Designing mobility models based on social network theory , 2007, MOCO.

[9]  George L. Lyberopoulos,et al.  Mobility modeling in third-generation mobile telecommunications systems , 1997, IEEE Wirel. Commun..

[10]  Imrich Chlamtac,et al.  End-to-end QoS framework for heterogeneous wired-cum-wireless networks , 2006, Wirel. Networks.

[11]  Ayari Aymen,et al.  Probabilistic model for mobility in cellular network subscriber , 2009, 2009 1st International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace & Electronic Systems Technology.

[12]  Jörg Ott,et al.  Working day movement model , 2008, MobilityModels '08.

[13]  David Kotz,et al.  Extracting a Mobility Model from Real User Traces , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.

[14]  Ahmed Helmy,et al.  A survey of mobility modeling and analysis in wireless adhoc networks , 2004 .

[15]  Hamid Sharif,et al.  A Quantitative Study of Mobility Impact for Real-Time Services on a Wi-Fi Multi-hop Network , 2008, VTC Spring 2008 - IEEE Vehicular Technology Conference.

[16]  S. Thipchaksurat,et al.  Effect of mobility on predictive mobility support dynamic resource reservation in cellular networks , 2008, 2008 8th International Conference on ITS Telecommunications.