Synchronizing videos over file-hosting services on personal cloud such as Dropbox, Box or Onedrive leads to wastage in bandwidth and storage, which can be critical, while using mobile devices. Users can alternatively download the video on-the-go, but that leads to high latency, depending on network bandwidth and video file size. In contrast, adaptive video streaming allows near-real-time viewing by streaming the best possible quality in a given network condition. This feature is achieved by keeping multiple versions of video in cloud, leading to additional costs in cloud storage. Moreover, current solutions can only support a small set of bitrates, leading to abrupt switches in video resolution especially when the network condition is unstable, as often experienced by mobile users. This paper introduces Vsync, a framework for cloud based video synchronization for mobile devices. A video content is streamed using a cloud-based real-time transcoding and transmission framework to provide smooth video quality. Built over prediction models for video transcoding sessions and a QoE based adaptive video streaming protocol, Vsync is able to obtain the improvements of 37 ~ 80% than other compared schemes. The dataset and evaluation was done on a pool of 220K video clips.

The quality of mobile videos is usually quantified through the Quality of Experience (QoE), which is usually based on network QoS measurements, user engagement, or post-view subjective scores. Such quantifications are not adequate for real-time evaluation. They cannot provide on-line feedback for improvement of visual acuity, which represents the actual viewing experience of the end user. We present a visual acuity framework which makes fast online computations in a mobile device and provide an accurate estimate of mobile video QoE. We identify and study the three main causes that impact visual acuity in mobile videos: spatial distortions, types of buffering and resolution changes. Each of them can be accurately modeled using our framework. We use machine learning techniques to build a prediction model for visual acuity, which depicts more than 78% accuracy. We present an experimental implementation on iPhone 4 and 5s to show that the proposed visual acuity framework is feasible to deploy in mobile devices. Using a data corpus of over 2852 mobile video clips for the experiments, we validate the proposed framework.

Wireless communication systems are highly prone to channel errors. With video being a major player in Internet traffic and undergoing exponential growth in wireless domain, we argue for the need of a Video-aware MAC (VMAC) to significantly improve the throughput and delay performance of real-time video streaming service. VMAC makes two changes to optimize wireless LAN for video traffic: (a) It incorporates a Perceptual-Error-Tolerance (PET) to the MAC frames by reducing MAC retransmissions while minimizing any impact on perceptual video quality; and (b) It uses a group NACK-based Adaptive Window (NAW) of MAC frames to improve both throughput and delay performance in varying channel conditions. Through simulations and experiments, we observe 56--89% improvement in throughput and 34--48% improvement in delay performance over legacy DCF and 802.11e schemes. VMAC also shows 15--78% improvement over legacy schemes with multiple clients.

Video telephony is becoming popular over smart-phones and tablets. Unlike the Desktop era, smartphone users are often `mobile' and this impacts how the video is processed and transmitted over the network. The significant increase in the motion content in such videos change the composition of video frames. Coupled with wireless packet losses, it often leads to poor quality of video received by the end user. In this work, we propose RVD, a framework for Reliable Video Delivery in mobile telephony by accounting for video object motion comprising foreground end-user motion and background scene changes in the network transmission of video. Multilayer perceptron (MLP) based non-linear regression model is used to analyze the impact of redundancy on received video quality under network variations and different degrees of video motion. RVD achieves 17-25% bandwidth savings for a target video quality, and 50-56% quality improvement over video-oblivious approaches.

Evolved Multimedia Broadcast Multicast Services (eMBMS) in LTE standards provides Raptor code as Forward Error Correction (FEC) scheme in application layer. Hybrid automatic repeat request (HARQ) is also used to increase reliability at MAC layer for packet recovery. The two mechanisms, with no interactions between them, may either lead to more redundancy in download link (DL) network resource or meaningless drops of recovery data at application layer. In this paper, we first analyze tradeoff between two recovery mechanisms and then present a probabilistic model to find optimal Raptor encoding rate and number of HARQ retransmissions for a given network condition. This can achieve a saving of upto 13-15% in DL network resources compared to existing schemes while ensuring reliable file delivery. It was also found to reduce the transmission delay (by minimizing the number of re-transmissions). The model was evaluated using LTE-A simulation framework.

Video quality assessment in mobile devices, for instances smart phones and tablets, raises unique challenges such as unavailability of original videos, the limited computation power of mobile devices and inherent characteristics of wireless networks (packet loss and delay). In this paper, we present a metric, Temporal Variation Metric (TVM), to measure the temporal information of videos. Despite its simplicity, it shows a high correlation coefficient of 0.875 to optical flow which captures all motion information in a video. We use the TVM values to derive a reduced-reference temporal quality assessment metric, Temporal Variation Index (TVI), which quantifies the quality degradation incurred in network transmission. Subjective assessments demonstrate that TVI is a very good predictor of users' Quality of Experience (QoE). Its prediction shows a 92.5% of correlation to subjective Mean Opinion Score (MOS) ratings. Through video streaming experiments, we show that TVI can also estimate the network conditions such as packet loss and delay. It depicts an accuracy of almost 95% in extensive tests on 183 video traces.

Eclipse and Netbeans are two top of the line Integrated Development Environments (IDEs) for Java development. Both of them provide support for a wide variety of development tasks and have a large user base. This paper provides an analysis and comparison for the compatibility and stability of Eclipse and Netbeans on the three most commonly used operating systems, Windows, Linux and Mac OS. Both IDEs are programmed in Java and use a Bugzilla issue tracker to track reported bugs and feature requests. We looked into the Bugzilla repository databases of these two IDEs, which contains the bug records and histories of these two IDEs. We used some basic data mining techniques to analyze some historical statistics of the bug data. Based on the analysis, we try to answer certain stability-comparison oriented questions in the paper, so that users can have a better idea which of these two IDEs is designed better to work on different platforms.