It is well known that PSNR does not always rank quality of an image or video sequence in the same way that a human being. There are many other factors considered by the human visual system and the brain. So, a lot of efforts were required to find an objective video quality metric that is able to measure the quality distortion similarly to the one perceived by the destination user. We analyze the behaviour of some of the most relevant objective quality metrics when they are applied to video compressed by a H264/AVC codec at different bit-rates and with error resilience options enabled. Video data is transmitted in a wireless MANET environment and packet losses are modelled for different scenarios including variable congestion and mobility states. We take as reference the PSNR metric and try to find out if there is a more accurate metric in terms of human quality perception that could substitute PSNR in the performance evaluation of different coding proposals under packet loss scenarios.

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This work presents our experience on Android teaching at Miguel Hernández University (Elche, Spain). We decided to orientate our courses toward Android app development, and encouraged students to carry their Android phones or tablets to the classroom. The results, in terms of student motivation, satisfaction and engagement in programming have been extraordinary.

With future vehicles equipped with processing capability, storage, and communications, vehicular networks will become a reality. A vast number of applications will arise that will make use of this connectivity. Some of them will be based on video streaming. In this paper we focus on HEVC video coding standard streaming in vehicular networks and how it deals with packet losses with the aid of RaptorQ, a Forward Error Correction scheme. As vehicular networks are packet loss prone networks, protection mechanisms are necessary if we want to guarantee a minimum level of quality of experience to the final user. We have run simulations to evaluate which configurations fit better in this type of scenarios.

Nowadays, more and more vehicles are equipped with communication capabilities, not only providing connectivity with onboard devices, but also with off-board communication infrastructures. From road safety (i.e., multimedia e-call) to infotainment (i.e., video on demand services), there are a lot of applications and services that may be deployed in vehicular networks, where video streaming is the key factor. As it is well known, these networks suffer from high interference levels and low available network resources, and it is a great challenge to deploy video delivery applications which provide good quality video services. We focus our work on supplying error resilience capabilities to video streams in order to fight against the high packet loss rates found in vehicular networks. So, we propose the combination of source coding and channel coding techniques. The former ones are applied in the video encoding process by means of intra-refresh coding modes and tile-based frame partitioning techniques. The latter one is based on the use of forward error correction mechanisms in order to recover as many lost packets as possible. We have carried out an extensive evaluation process to measure the error resilience capabilities of both approaches in both (a) a simple packet error probabilistic model, and (b) a realistic vehicular network simulation framework. Results show that forward error correction mechanisms are mandatory to guarantee video delivery with an acceptable quality level , and we highly recommend the use of the proposed mechanisms to increase even more the final video quality.