Search

Multimedia streaming over multihop ad hoc networks is an emerging application. Consequently, a novel routing algorithm designed for video streaming is necessary. Existing video routing solutions focus on technical parameters such as bandwidth, jitter, delay, etc. However, these parameters are not perfectly correlated to the quality of experience (QoE) perceived by users. In this paper, we study a QoE-based routing problem in multihop wireless ad hoc networks. We use the pseudosubjective quality assessment (PSQA) tool to derive the mean opinion score (MOS). As PSQA is an implicit nonlinear function, we use a linearization method to derive its approximate mathematical form. Then, we formulate the optimal QoE-based routing problem as a mixed-integer linear programming. We show major properties of feasible solutions of the problem and then exploit them to propose a heuristic algorithm. The results show that the proposed algorithm can enhance resource utilization and the users' experience with wireless networks. Moreover, the feasible solution can be reached in real time using the proposed heuristic approach. [ABSTRACT FROM PUBLISHER]

Multi-hop relaying combined with scarcity of wireless resources in ad-hoc networks can deteriorate the quality of service. As a result, one of the major challenges in video streaming over ad-hoc networks is enhancing users' experience and network utilization. The emergence of scalable video coding standard enables smooth adaptation of video quality to network conditions. In this paper, we study two optimization problems: (1) maximize the global quality of experience of all users and (2) maximize the number of qualified streams. We formulate the both problems as mixed integer linear programming problems. These optimization problems are shown to be NP-hard. Consequently, we propose heuristic algorithms to solve them. Simulation results show that the proposed algorithms can provide the near-optimal video quality while the calculation times are much shorter than the one of optimal solution. [ABSTRACT FROM AUTHOR]

Self-driving vehicles are regarded as the future of transportation. Collecting more datasets will help to improve self-driving car modelling using data analysis; however, an incremental approach has to be taken for in-depth exploration of data analysis techniques applied to self-driving vehicles. Data analysis technologies for self-driving vehicles are expected to cover the current state of the art and highlight remaining challenges and barriers to the development of self-driving vehicles as part of intelligent transportation systems. [Extracted from the article]