Cloud storage systems are emerging as the primary solution for online storage and information sharing. As the demand for such a service is increasing at a phenomenal rate, the cost for maintaining and delivering content concerns the cloud providers and ISPs. As in other distributed systems, e.g., file sharing and multimedia streaming, network coding can significantly simplify the process for content distribution and retrieval. However, it also raises difficulties in updating portions of a file, as any change in the file will impact all coded content in the system. In this paper, we present the differential update model and its optimization for updating coded blocks by delivering only the changes in a file. We complete the design with an update algorithm and a communication protocol among all participants in the system. Our experimental results verify that our design makes network coding practical for file updates in cloud storage systems. The proposed update model leads to bandwidth saving, compared to conventional update mechanisms, with minimal computational costs.

To improve the quality of video streaming subject to video bitrate or communication channel capacity, a high-quality video is encoded into multiple layers of unequal importance. Layers that provide higher quality rely on the previous layers for successful reconstruction of transmitted video packets. Hence, if a video packet in a reference layer is corrupted or lost during transmission, the dependent layers cannot be reconstructed successfully, and the resources consumed to transmit them are wasted. To address this problem, unequal error protection (UEP) techniques have been proposed to provide appropriate level of protection to each layer according to their importance. Nonetheless, the importance of a piece of video content is determined by not only the layering structure, but also coding dependency imposed by encoding decisions. In this paper, based on a deep inspection of coding and prediction in SVC (a layered video coding standard) and an analysis of seven real SVC videos, we conclude that macro block-level coding dependency will provide a more accurate importance measure when applying UEP to protection video packets in noisy channels.

In recent years, the demand for high quality video streaming services has been growing significantly. Distributed video transcoding in cloud, i.e., re-encoding the source video to best match the capabilities of the network connection and the playback device in cloud and sending each user a tailored version of the video, is a recent solution for fast and high quality video streaming services. In such a transcoding scheme, video is segmented into chunks of equal size and the chunks are distributed among multiple virtual machines for parallel transcoding. The transcoded chunks are then merged together to create the new transcoded video appropriate for playback on specific end-user devices. In this paper, we conduct a performance analysis of the impact of chunk size on coding efficiency and transcoding time. We observe that transcoding with larger chunks leads to better coding efficiency (i.e., lower bitrate) by trading off the transcoding time. The improvement in coding efficiency and the level of trade-off in transcoding time highly depend on the visual similarity among frames of a video sequence. From the analysis, we suggest that for better coding efficiency and faster transcoding, the chunk size should be dynamically adjusted according to the visual similarity.

To improve the quality of experience of video streaming services, content providers are challenged by the need to prepare videos at different quality levels appropriate to the network infrastructure and device hardware specification. Distributed video transcoding in the cloud has received many research attentions to address this challenge. Such a cloud-based solution segments a video into multiple video chunks and distributes chunks to virtual machines in the cloud for parallel transcoding. However, by inspecting video codec standards, we learn that important inter-dependency among video frames is broken if the video is segmented into fixed-size chunks, which leads to increasing bitrate and transcoding time. In this paper, we propose a distributed video transcoding scheme that exploits dependency among GOPs by preparing video chunks of variable size. Experimental results from real video sequences with diverse visual features show that the proposed transcoding scheme effectively reduces bitrate and transcoding time.

Layered video coding standards encode a high-quality video into multiple layers of unequal importance. Dependent layers that provide higher quality rely on their respective reference layers for successful reconstruction of transmitted video frames. Hence, if a video packet in a reference layer is corrupted or lost during transmission, all its dependent layers cannot be reconstructed successfully, and the resources consumed to transmit them are wasted. To address this problem, unequal error protection (UEP) techniques have been proposed to provide protection to each layer according to their importance. Nonetheless, the importance of a piece of video content is determined by not only the layering structure, but also visual features and encoding decisions. In this paper, we look deeper into the coding and prediction structure of layered encoded videos and model the the dependency among macroblocks and submacroblocks (the finest processing units of H.264 video coding standard) as a weighted graph. Based on this graph, we propose a dependency-aware UEP model that protects macroblocks according to their importance. Our simulation results show that the proposed UEP model outperforms the conventional UEP models for layered SVC videos by 3.76 dB of peak signal-to-noise ratio (PSNR) when the channel packet loss rate is as high as 28%.

With increasing deployment of 3G/4G technologies and growing computing power of modern smartphones, video multicast over mobile networks is becoming very popular. Due to mobility and interference, the channel quality fluctuates frequently over time, making it challenging to deliver live streaming services to mobile devices. In this paper, we propose an adaptive video streaming system that delivers layered video content to mobile devices over wireless channels of different and fluctuating qualities. To this end, the proposed system employs a novel design to prepare the redundant coded blocks required for forward error correction. This design makes the system flexible to dynamic changes in loss rates with minimum coding overhead. Specifically, the employed coding scheme is empowered by a novel fine granular coefficient matrix that decreases the delay and the computational complexity of coding operations. Furthermore the simulation results show that the new system offers a flexible streaming service, decreases the computational cost of preparing the FEC codes, significantly decreases the video transmission delay, and finally conserves energy on mobile devices.

While the demand for multimedia streaming, especially from mobile devices, is growing rapidly, mobile devices are challenged by the variable transmission rate in wireless channels and limited battery capacity. In this paper, we propose an energy-efficient cooperative streaming system. In the proposed system, mobile devices collectively stream a copy of the multimedia content from the source over cellular links. The devices form a cooperative group and share received content, in coded form for efficient loss detection and recovery, with each other over short-range links. The sharing is guided by the optimal rate allocation and scheduling (RAS) algorithm that determines the amount of data and the data to be transmitted on each link. Overall, the system minimizes both the streaming traffic in the cellular network and the energy consumed by streaming applications on mobile devices. Our experimental results also show that the RAS algorithm prolongs the streaming session for the entire cooperative group.

The continuous developments and improvements of network infrastructure along with the growing number of modern smartphones, tablets and smart TVs have led to an increasing popularity of multimedia applications, such as video conferencing, video streaming and mobile TV. Towards delivering video stream to diverse devices over a heterogamous network, scalable video coding (SVC) has received many research attentions. SVC is an extension of H.264/AVC that allows a video streaming service provider to encode a high quality video into a number of scalable layers. The receivers of the stream may decode the video at the appropriate quality level that is suitable for their hardware/software capabilities and network connections. Nevertheless, compared to single layer H.264/AVC, the de facto standard for many commercial streaming service providers such as YouTube, SVC is not widely deployed, mostly due to its overhead in terms of bit rate and complexity. In this paper, we conduct a thorough study on the performance of SVC for full HD video streaming. Our performance analysis identifies good and bad uses of SVC, quantifies the coding overhead, and benchmarks the SVC video quality under different spatial, temporal, and quality settings. Through the use a set of carefully selected and diverse video sequences, we also identify the types of video that can benefit from SVC.

Due to the increasing deployment of 3G/4G technologies and the growing computing power of modern smart-phones, video streaming is one of the most popular applications on these devices. In this paper, we propose an energy-efficient multimedia streaming system for smartphones. This system simultaneously utilizes two communication channels: cellular links carrying streaming content from the media Cloud to smartphones and WiFi links enabling cooperation among smartphones. There are three key contributions. First, the system copes with the variable loss rate and bandwidth fluctuation, common problems in wireless communication, by taking advantage of a novel two-level coding scheme. Second, the system reduces energy consumption due to network coding operations, by utilizing the copious computing resources in the Cloud. Consequently, XOR-only network coding is sufficient for sharing among smartphones. Third, we propose a light-weight distributed scheduling algorithm to manage collaboration and content sharing among the neighbouring nodes. The proposed local dissemination scheme, along with the proposed two-level coding scheme, outperforms state-of-the-art streaming systems in cellular networks. Our experimental results show that our proposed system saves up to 73% of battery usage on each phone compared to the current streaming mechanism over a 3G network. In addition, the system is capable to support higher quality streaming and decrease the streaming delay.

In recent years, cloud storage systems have emerged as the primary solution for online storage and information sharing. Due to efficient storage and bandwidth utilization, the use of erasure codes and network coding is proven to effectively provide fault tolerance and fast content retrieval in cloud storage systems. In a nutshell, coded blocks are distributed among storage nodes, and file retrieval is accomplished by downloading sufficient coded blocks from any group of storage nodes. However, due to high correlation between coded blocks and the original file, even a single-byte update invalidates all coded blocks in the system. In this paper, we introduce DeltaNC, a new differential update algorithm that keeps all coded blocks in a network-coding-based cloud storage system synchronized by transmitting only the changes in the file. Our experimental results, from a trace-driven simulator, show that DeltaNC significantly reduces the bandwidth and CPU usage and its performance is comparable to that offered by the Diff program, the common tool for updating files.