Distributed Rate Allocation Policies for Multi-Homed Video Streaming over Heterogeneous Access Networks

We consider the problem of rate allocation among multiple simultaneous video streams sharing multiple heterogeneous access networks. We develop and evaluate an analytical framework for optimal rate allocation based on observed available bit rate (ABR) and round-trip time (RTT) over each access network and video distortion-rate (DR) characteristics. The rate allocation is formulated as a convex optimization problem that minimizes the total expected distortion of all video streams. We present a distributed approximation of its solution and compare its performance against H-infinity optimal control and two heuristic schemes based on TCP-style additive-increase-multiplicative decrease (AIMD) principles. The various rate allocation schemes are evaluated in simulations of multiple high-definition (HD) video streams sharing multiple access networks. Our results demonstrate that, in comparison with heuristic AIMD-based schemes, both media-aware allocation and H-infinity optimal control benefit from proactive congestion avoidance and reduce the average packet loss rate from 45% to below 2%. Improvement in average received video quality ranges between 1.5 to 10.7 dB in PSNR for various background traffic loads and video playout deadlines. Media-aware allocation further exploits its knowledge of the video DR characteristics to achieve a more balanced video quality among all streams.Comment: 12 pages, 22 figure

Load sediments quantification in Algerian North-West basins by ANN (Artificial Neurons Network) method

Due to the complexity of basins morphometric parameters and the hydroclimatic irregularity of the semi-arid regions of North Africa, solid transport has been still far from being clearly assessed. This study attempts to shed light on this problem; in order to conceive a global model for the suspended sediment load quantification, taking into account all stream waters of the North-West area of Algerian. The calculation is based on the use of the ANN artificial neurons network method, which has proven its success and its reliability in several fields of research. The collected data are measured in hydrometric stations of several basins, such as Cheliff, Tafna, Macta, and Oran’s basins. The results obtained using the ANN method are sufficiently reliable, the best correlations were obtained for each studied stream water exceeding 97% (specific model to each station), and 90% in the case of a global model characterizing for all studied stations, which allows the extracted model to give better estimation of the suspended solid flow rates for any measured liquid flow rate of the north-west Algerian basins

Saving Energy in Mobile Devices for On-Demand Multimedia Streaming -- A Cross-Layer Approach

This paper proposes a novel energy-efficient multimedia delivery system called EStreamer. First, we study the relationship between buffer size at the client, burst-shaped TCP-based multimedia traffic, and energy consumption of wireless network interfaces in smartphones. Based on the study, we design and implement EStreamer for constant bit rate and rate-adaptive streaming. EStreamer can improve battery lifetime by 3x, 1.5x and 2x while streaming over Wi-Fi, 3G and 4G respectively.Comment: Accepted in ACM Transactions on Multimedia Computing, Communications and Applications (ACM TOMCCAP), November 201

DRS: Dynamic Resource Scheduling for Real-Time Analytics over Fast Streams

In a data stream management system (DSMS), users register continuous queries, and receive result updates as data arrive and expire. We focus on applications with real-time constraints, in which the user must receive each result update within a given period after the update occurs. To handle fast data, the DSMS is commonly placed on top of a cloud infrastructure. Because stream properties such as arrival rates can fluctuate unpredictably, cloud resources must be dynamically provisioned and scheduled accordingly to ensure real-time response. It is quite essential, for the existing systems or future developments, to possess the ability of scheduling resources dynamically according to the current workload, in order to avoid wasting resources, or failing in delivering correct results on time. Motivated by this, we propose DRS, a novel dynamic resource scheduler for cloud-based DSMSs. DRS overcomes three fundamental challenges: (a) how to model the relationship between the provisioned resources and query response time (b) where to best place resources; and (c) how to measure system load with minimal overhead. In particular, DRS includes an accurate performance model based on the theory of \emph{Jackson open queueing networks} and is capable of handling \emph{arbitrary} operator topologies, possibly with loops, splits and joins. Extensive experiments with real data confirm that DRS achieves real-time response with close to optimal resource consumption.Comment: This is the our latest version with certain modificatio

Low-power, 10-Gbps 1.5-Vpp differential CMOS driver for a silicon electro-optic ring modulator

We present a novel driver circuit enabling electro-optic modulation with high extinction ratio from a co-designed silicon ring modulator. The driver circuit provides an asymmetric differential output at 10Gbps with a voltage swing up to 1.5V(pp) from a single 1.0V supply, maximizing the resonance-wavelength shift of depletion-type ring modulators while avoiding carrier injection. A test chip containing 4 reconfigurable driver circuits was fabricated in 40nm CMOS technology. The measured energy consumption for driving a 100fF capacitive load at 10Gbps was as low as 125fJ/bit and 220fJ/bit at 1V(pp) and 1.5V(pp) respectively. After flip-chip integration with ring modulators on a silicon-photonics chip, the power consumption was measured to be 210fJ/bit and 350fJ/bit respectively