With the increasing demand of media-rich entertainment and location-aware services from people on the road, how to disseminate the multimedia data in large-scale Vehicular Ad-Hoc Networks (VANETs) efficiently and reliably is a pressing issue. Due to the high mobility, large scale, and limited contact time between vehicles, it is quite challenging to support the multimedia data dissemination in VANETs. In this paper, we first utilize a hybrid framework to model the VANETs to address the mobility and scalability issues. Then, we formulate a utility-based maximization problem to find the best delivery strategy and select an optimal path for the multimedia data dissemination, where the utility function has taken the delivery delay, Quality of Services (QoS), and storage cost into consideration. With rigorous analysis, we obtain the closed-form of the expected utility of a path, and then obtain the optimal solution of the problem with the convex optimization theory. Finally, we conduct trace-driven simulations to evaluate the performance of the proposed algorithm with real traces collected by taxis in Shanghai. The simulation results demonstrate the rigorousness of our theoretical analysis, and the effectiveness of the proposed solution.

How to coordinate multiple Electric Vehicles' (EVs) charging demands to satisfy the requirements of the customers and also maximize the profit for the charging station is an important and challenging problem. Most of the existing works mainly focus on the interest of one side. In this paper, we develop a utility based multi-charger framework to ensure a win-win situation for both the customers and the charging station. We first propose an admission control algorithm to guarantee that the non-flexible charging requirements of all admitted EVs can be satisfied before their departure time. Then, we encourage all EVs to take more charging flexibility by setting an attractive price for the additional electricity to be taken by the EVs. To maximize the profit of the charging station, a utility based charging scheduling algorithm is proposed. Extensive simulations based on practical EV charging information have been conducted, which demonstrate the effectiveness of the proposed algorithms. The results show that the proposed approach can outperform the state-of-the-art one in terms of total utility, so that the charging station can enjoy a higher profit and the customers can enjoy more cost savings.

In cognitive vehicular ad hoc networks (CVANETs), channel conditions are highly dynamic due to both vehicle mobility and primary user activity. In this paper, to support high-quality video playback in such a challenging scenario, an adaptive video streaming algorithm built on scalable video coding (SVC) is proposed for reducing interruption ratio and improving visual quality. The proposed streaming algorithm is capable of deciding the proper number of video layers for vehicle users, by taking into account several important factors including vehicle position, velocity, the activity of primary users. Simulation results demonstrate the superiority of the proposed algorithm on playback interruption ratio and visual quality over the compared algorithm.

Video streaming is gaining popularity among mobile users. The latest mobile devices, such as smart phones and tablets, are equipped with multiple wireless network interfaces. How to efficiently and cost-effectively utilize multiple links to improve video streaming quality needs investigation. In order to maintain high video streaming quality while reducing the wireless service cost, in this paper, the optimal video streaming process with multiple links is formulated as a Markov Decision Process (MDP). The reward function is designed to consider the quality of service (QoS) requirements for video traffic, such as the startup latency, playback fluency, average playback quality, playback smoothness and wireless service cost. To solve the MDP in real time, we propose an adaptive, best-action search algorithm to obtain a sub-optimal solution. To evaluate the performance of the proposed adaptation algorithm, we implemented a testbed using the Android mobile phone and the Scalable Video Coding (SVC) codec. Experiment results demonstrate the feasibility and effectiveness of the proposed adaptation algorithm for mobile video streaming applications, which outperforms the existing state-of-the-art adaptation algorithms.

Nowadays, peer-assisted video on-demand (PA-VoD) systems offer high-definition (HD) channels to online users. However, the quality of service in such HD channels is usually not comparable to the standard-definition (SD) ones, as HD channels have to seek more bandwidth support and cache space from peers, which is a challenging task. In this paper, we focus on peer cache and upload bandwidth management at the same time for multi-channel PA-VoD systems with heterogeneous video playback rates, i.e., HD and SD channels coexist with different bandwidth and cache requirements. We first take user viewing behaviors into account and derive the statistical performance bounds on server bandwidth consumption, which lead to the conclusion that such behaviors can easily affect the provisioning for HD channels, even if there is enough upload bandwidth from SD peers. We then formulate bandwidth allocation as a linear programming problem to calculate the tight lower bound at any time instant, with global information available and system-wide coordination possible (e.g., through a tracker). Next, we design heuristic algorithms for peer cache replacement and upload bandwidth allocation to fit with the nature of a P2P structure, and the results are compared with the statistical and instance performance bounds through extensive simulation, which shows the efficacy of the proposed algorithms in dynamic scenarios.

Video streaming is gaining popularity among mobile users. The latest mobile devices, such as smart phones and tablets are equipped with multiple wireless network interfaces. How to efficiently and cost-effectively utilize multiple links to improve the video streaming quality needs to be investigated. In order to maintain high video streaming quality while reduce the wireless service cost, in this paper, the optimal video streaming process with multiple links is formulated as a Markov Decision Process (MDP). The reward function is designed to consider the quality of experience (QoE) requirements for video traffic, such as the interruption rate, average playback quality, playback smoothness and wireless service cost. Using dynamic programming, the MDP can be solved to obtain the optimal streaming policy. To evaluate the performance of the proposed multi-link rate adaptation (MLRA) algorithm, we implement a testbed using the Android mobile phone and the open-source X264 video codec. Experimental results demonstrate the feasibility and effectiveness of the proposed MLRA algorithm for mobile video streaming applications, which outperforms the existing state-of-the-art one.

Video is a desirable medium to provide traffic information, news, advertisements, etc. to people on-the-road. Due to the high mobility and dynamic topology of VANET, an open question is whether it is feasible to support video streaming services using license-free wireless communications between vehicles and road-side-units. In this paper, using the advanced scalable video coding (SVC), we propose an adaptive video streaming scheme for video streaming services in the highway scenario. Relying on cooperative relay among vehicles, a vehicle can download video data using a direct link or a multi-hop path to the RSUs. Considering the current download speed and the receiver buffer level, the proposed scheme can request an appropriate number of video enhancement layers to improve video quality of experience (QoE). Simulation results with real video traces have demonstrated the effectiveness of the proposed scheme which can make a good tradeoff among the perceived video quality, the startup latency and the interruption ratio.