Energy consumption analysis of cloud-based video games streaming to mobile devices

Within the growing of the gaming industry and mobile technologies, cloud-based video games streaming to mobile devices gains popularity fast. However, several issues and challenges such as user responsiveness, video quality, service quality, operating cost and energy consumption have to be addressed. Previous research studies did not thoroughly investigate the energy consumption of mobile devices used for cloud gaming.

This paper focuses on how mobile device energy consumption is impacted by video game content characteristics, transmission protocol and wireless network type. The results show that game content characteristics impact energy consumption (i.e., up to 47% between games at maximum brightness for OLED screens), UDP is more energy efficient than TCP (i.e., up to 13.6%), while WiFi is more energy efficient than 3G (i.e., up to 40%). The outcome of this study can provide beneficial input for adaptive energy efficient cloud-based video games streaming mechanisms.

Anticipatory quality-resource allocation for multi-user mobile video streaming

Mobile video delivery forms the largest part of the traffic in cellular networks. Thus optimizing the resource allocation to satisfy a user’s quality of experience is becoming paramount in modern communications. This paper belongs to the line of research known as anticipatory networking that makes use of prediction of wireless capacity to improve communication performance.

In particular, we focus on the problem of optimal resource allocation for steady video delivery under maximum average quality constraints for multiple users. We formulate the problem as a piecewise linear program and provide a heuristic algorithm, which solution is close to optimal. Based on our formulation we are now able to trade off minimum video quality, average quality and offered network capacity.

Transmission distortion modeling for view synthesis prediction based 3-D video streaming

View synthesis prediction (VSP) is an important tool for improving the coding efficiency in the next generation three-dimensional (3-D) video systems. However, VSP will result in a new type of inter-view error propagation when the multi-view video plus depth (MVD) data are transmitted over the lossy networks. In this paper, this new type of error propagation is characterized and modeled.

Firstly, a new analytic model is formulated to estimate the expected transmission distortion caused by error propagation from the synthesized reference view. Then, the compound impact of the transmission distortions of both the texture video and the depth map on the quality of the synthetic reference view is mathematically analysed. Our extensive simulation results demonstrate that the proposed transmission distortion model is very accurate.

Adaptive Layer Switching Algorithm Based on Buffer Underflow Probability for Scalable Video Streaming Over Wireless Networks

Scalable Video Coding (SVC) has been raised as a promising technique to enable flexible videotransmission for mobile users with heterogeneous terminals and varying channel capacities. In this paper, we design an adaptive layer switching algorithm for on-demand scalable video service based on receiver’s buffer underflow probability (BUP). Since the low quality of channel may lead to a low buffer fullness, the buffer fullness is an indicator for reflecting the channel condition, and we define BUP for characterizing the mismatch between the video bitrate and the channel throughput. Accordingly, the adaptive SVC transmission problem is formulated as the adaptive adjustment of video layers based on BUP. This allows us to optimize the attainable video quality, while keeping BUP below a desired level.

In order to estimate BUP, we derive an analytical model based on the large deviation principles. Then, an online layer switching algorithm is proposed using this estimation model, which is capable of accommodating different channel qualities without any prior knowledge of the channel variations and of the video characteristics. We further introduce a perturbation-based layer switching approach for reducing the quality fluctuating issue caused by frequent layer switches, thus improving the viewer’s QoE. A system prototype is implemented to evaluate the success of the proposed method.We also conduct simulations in multiuser scenarios with real video traces and the results demonstrate that the proposed algorithm is capable of improving the playback experience, while keeping a low playback interruption rate and quality variation.

Optimal Rate Allocation for Video Streaming in Wireless Networks With User Dynamics

We consider the problem of optimal rate allocation and admission control for adaptive video streamingsessions in wireless networks with user dynamics. The central aim is to achieve an optimal tradeoff between several key objectives: maximizing the average rate utility per user, minimizing the temporal rate variability, and maximizing the number of users supported. We derive sample path upper bounds for the long-term net utility rate in terms of either a linear program or a concave optimization problem, depending on whether the admissible rate set is discrete or continuous.

We then show that the upper bounds are asymptotically achievable in large-scale systems by policies which either deny access to a user or assign it a fixed rate for its entire session, without relying on any advance knowledge of the duration. Moreover, the asymptotically optimal policies exhibit a specific structure, which allow them to be characterized through just a single variable, and have the further property that the induced offered load is unity. We exploit the latter insights to devise parsimonious online algorithms for learning and tracking the optimal rate assignments and establish the convergence of these algorithms. Extensive simulation experiments demonstrate that the proposed algorithms perform well, even in relatively small-scale systems.

No-reference video quality metric for streaming service using DASH standard

This work proposes a no-reference video quality metric that considers two parameters, pauses and changes in video resolution. Results indicate that users’ Quality-of-Experience (QoE) is highly correlated with these parameters.

The proposed metric has low complexity because it is based on application-level parameters; it can, therefore, be easily implemented in consumer electronic devices.

Color spaces and histogram distances influence on people re-identification in video stream

The influence of color spaces and histogram distances on object re-identification in video stream is investigated.

Dataset with images of pedestrians is being analyzed. A set of color spaces, histograms types and distance functions is applied to minimize re-identification errors by calculating false acceptance and false rejection error rates.

Video streaming in autonomous Mobile Robot using Wi-Fi

An Autonomous Mobile Robot is used to travel from one source to destination using two or more wheels. In order to reach a target where the person could not reach that particular destination, they must depend on some non-human called Mobile Robots. The Proposed work contains Autonomous Arduino Yun four wheeled mobile robot is allowed to move from source to destination position for finding a path, avoiding the obstacles and Video Streaming.

These are achieved using a Wi-Fi technology, by avoiding the existing work with Bluetooth technology which is less secure and covers only shorter range. The best path is identified by using an Aggrandized Genetic Algorithm (AGA) which is best compared to few other algorithms. In order to achieve a secure communication and travelling to a longer distance when critical situation occurs to the person for reaching a destination from source, the Wi-Fi (IEEE 802.11 b/g/n) is useful for reaching the task.

Hierarchical QoE model for wireless video streaming with fountain codes

The rapid growth of video in wireless networks is a crucial issue to be addressed by content providers. Nowadays, an emerging and promising trend is the development of solutions aimed at maximizing the quality of experience (QoE) of end users. However, the prediction of the QoE perceived by users in different conditions remains a major challenge. In this paper, we propose a two-layer hierarchical QoE model for wireless video streaming systems that employ fountain codes in forward error correction (FEC).

The first layer decomposes the impact of fountain codes on the user QoE into three factors, namely the initial playback delay, the rebuffering frequency and the rebuffering time. The second layer of the QoE model relates the user QoE in terms of the mean opinion score (MOS) to the aforementioned three factors using a quadratic function. An exemplary application of the developed QoE model in the admission control of a bandwidth-limited video on demand (VoD) system is given with the goal toward guaranteeing the QoE quality of all users. Simulation results confirms the effectiveness of the developed admission control technique.

Experiences from a field test using ICN for live video streaming

Information Centric Networking (ICN) aims to evolve the Internet from a host-centric to a data-centric paradigm. In particular, it improves performance and resource efficiency in events with large crowds where many users in a local area want to generate and watch media content related to the event. In this paper, we present the design of a live video streaming system built on the NetInf ICN architecture and how the architecture was adapted to support live streaming of media content.

To evaluate the feasibility and performance of the system, extensive field tests were carried out over several days during a major sports event. We show that our system streams videos successfully with low delay and communication overhead compared with existing Internet streaming services, by scalability tests using emulated clients we also show that it could support several thousands of simultaneous users.