Revenue-driven video delivery in vehicular networks with optimal resource scheduling

Abstract Vehicular network is the enabler of various services for transportation means including safety and infotainment applications. It can better deal with the highly dynamic nature of roads and highways. Meanwhile, the existing infrastructure of the cellular networks is unable to cope with the heavy traffic imposed mainly by videos. Thus, the cellular operators may leverage Roadside Untis (RSU) to offload video traffic and provide better quality of experience for vehicle customers. In this paper, we propose a pricing model that motivates RSU owners to cooperate with the cellular operators in order to offer better quality of experience (QoE) and offload substantial part of videos traffic from the cellular networks. The pricing model also motivates the vehicle owners to cooperate with the RSU to deliver contents to each other and aims to maximize RSU revenue and provide a wide range of QoS levels to the content providers in order to give them a degree of freedom to choose the suitable level of quality for their customers and based on their allocated budgets. We prove this problem is hard to solve, then we propose a lightweight greedy methods as alternative solutions. The conducted results show the efficiency of the proposed solutions and their ability to obtain results similar or so close to the optimal solutions.

[1]  Athanasios V. Vasilakos,et al.  Information-centric cost-efficient optimization for multimedia content delivery in mobile vehicular networks , 2017, Comput. Commun..

[2]  Song Guo,et al.  D2D-based content delivery with parked vehicles in vehicular social networks , 2016, IEEE Wireless Communications.

[3]  Yousof Al-Hammadi,et al.  A Stackelberg game for street-centric QoS-OLSR protocol in urban Vehicular Ad Hoc Networks , 2018, Veh. Commun..

[4]  Cheng-Xiang Wang,et al.  5G Ultra-Dense Cellular Networks , 2015, IEEE Wireless Communications.

[5]  Fredrik Tufvesson,et al.  5G: A Tutorial Overview of Standards, Trials, Challenges, Deployment, and Practice , 2017, IEEE Journal on Selected Areas in Communications.

[6]  Susana Sargento,et al.  Mobility Prediction-Assisted Over-the-Top Edge Prefetching for Hierarchical VANETs , 2018, IEEE Journal on Selected Areas in Communications.

[7]  Yi Zhou,et al.  A Fuzzy-Rule Based Data Delivery Scheme in VANETs with Intelligent Speed Prediction and Relay Selection , 2018, Wirel. Commun. Mob. Comput..

[8]  Zhu Han,et al.  Distributed Relay Selection and Power Control for Multiuser Cooperative Communication Networks Using Stackelberg Game , 2009, IEEE Transactions on Mobile Computing.

[9]  Hongli He,et al.  Resource Allocation for Video Streaming in Heterogeneous Cognitive Vehicular Networks , 2016, IEEE Transactions on Vehicular Technology.

[10]  Lin Zhang,et al.  Bus-Ads: Bus Trajectory-Based Advertisement Distribution in VANETs Using Coalition Formation Games , 2017, IEEE Systems Journal.

[11]  Ulrich Pferschy,et al.  Resource allocation with time intervals , 2010, Theor. Comput. Sci..

[12]  Mingquan Wu,et al.  On Accelerating Content Delivery in Mobile Networks , 2013, IEEE Communications Surveys & Tutorials.

[13]  Hongli He,et al.  Channel Allocation for Adaptive Video Streaming in Vehicular Networks , 2017, IEEE Transactions on Vehicular Technology.

[14]  Weihua Zhuang,et al.  Traffic Offloading for Online Video Service in Vehicular Networks: A Cooperative Approach , 2018, IEEE Transactions on Vehicular Technology.

[15]  Louiza Bouallouche-Medjkoune,et al.  Geographic routing protocols for Vehicular Ad hoc NETworks (VANETs): A survey , 2018, Veh. Commun..

[16]  Song Guo,et al.  A Game Theoretic Approach to Parked Vehicle Assisted Content Delivery in Vehicular Ad Hoc Networks , 2017, IEEE Transactions on Vehicular Technology.

[17]  Zhou Su,et al.  Optimal Access Control in Heterogeneous Vehicular Networks: A Game Theoretic Approach , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[18]  Miguel Sepulcre,et al.  Analytical Models of the Performance of C-V2X Mode 4 Vehicular Communications , 2018, IEEE Transactions on Vehicular Technology.

[19]  Lin Yao,et al.  A Cooperative Caching Scheme Based on Mobility Prediction in Vehicular Content Centric Networks , 2018, IEEE Transactions on Vehicular Technology.

[20]  Patrick Maillé,et al.  Price competition between road side units operators in vehicular networks , 2014, 2014 IFIP Networking Conference.

[21]  Francesco Malandrino,et al.  Scheduling Advertisement Delivery in Vehicular Networks , 2018, IEEE Transactions on Mobile Computing.