Rate control in video compression adjusts the encoding parameters to reach a certain target bitrate for the encoded video. State-of-the-art rate controllers for hybrid video coding typically employ content-dependent video bitrate models and video quality metrics (VQMs). To capture the content characteristics, temporal and spatial video activity measures are determined from the raw video using computationally complex algorithms that require access to the uncompressed source video. In automotive deployments, however, full access to the uncompressed source video and the internal functions of video encoders is typically not possible. As a remedy, in this paper, we present a low-complexity approach to estimate the temporal activity (TA) and spatial activity (SA) measures for videos that are captured by a front-facing camera of a vehicle, based on the context information of the vehicle. To this end, we exploit information about the dynamics of the vehicle and other vehicles in the field-of-view of the front-facing camera. We apply the estimated TA and SA values to a video bitrate model and an objective VQM and use these models to solve the rate control problem to determine the optimal encoding settings for given bitrate constraints. The proposed low-complexity solution offers a similar accuracy in achieving rate constraints and similar perceptual quality characteristics as a solution that uses the computed TA and SA values, with the advantage that no access to the uncompressed source video stream or the internal functions of the video encoder is required.

We study the uplink delivery of live video using adaptive HTTP streaming (AHS). In AHS, the process of simultaneously creating video levels at different rates is computationally demanding and quickly exceeds the computational capacity of mobile devices. As a remedy, we propose a network-aware video level selection approach which reduces the number of levels that need to be encoded. To this end, we develop an algorithm which selects a reduced set of video levels from a static pre-defined set based on TCP uplink throughput information. More specifically, during session start-up and after inter-RAN handovers, TCP uplink throughput information from a remote database is used while otherwise, actual TCP uplink throughput measurements are performed. We test the proposed approach in an automotive scenario to upstream the video of a vehicle's front-facing camera to a remote video portal. Our results show that our proposed network-aware video level selection approach leads to a significant reduction of the number of video levels that need to be encoded. At the same time, a similar quality of experience is achieved in terms of mean subjective quality of the delivered video segments, interrupted playback duration due to stalling events, and number of quality switches when compared to an implementation which considers the static full set of video levels.

We present a low complexity approach for the estimation of the temporal and spatial activity parameters of videos which are captured by a front-facing camera of a vehicle based on context information of the vehicle. The estimated parameters are integrated into an objective video quality metric, which can be used to determine the perceptual quality of a compressed video stream. Our proposed video quality metric has very low computational complexity, which makes it suitable for live video streaming applications. It shows a high Pearson correlation of 0.98 with an average root-mean-square error of 6%, as verified by statistical analysis with data from subjective tests.

The invention discloses a method and a compression device for compressing a stream of data from a source to at least a sink comprising the steps of: - determining at least a first bandwidth and a second bandwidth of a network, which are the source for transmitting the data stream to at least one sink is available; and - compressing the data stream with at least a first compression ratio to a first compressed data stream and a second compression rate to a second compressed data stream, wherein said first compression rate is chosen so that it is optimized for transmission with the first bandwidth and the second compression rate so is chosen such that it is optimized for transmission at the second bandwidth; and - simultaneously providing the first compressed data stream and the second compressed data stream

We present a novel analytical bit rate model for H.264/AVC video encoding which is based on the quantization parameter, the frame rate, the group-of-pictures length, the group-of-pictures structure and content-dependent parameters. The content-dependent parameters are described as a function of two standard video activity measures (temporal and spatial activity) which can easily be determined from the uncompressed video. Our bit rate model shows a Pearson correlation of more than 0.97 and a root-mean-square error of less than 2% as determined by statistical analysis.

We present a novel bit rate model for H.264/AVC video encoding which is based on the quantization parameter, the frame rate as well as temporal and spatial activity measures. With the proposed model, it is possible to trade-off the frame rate versus the quantization parameter to achieve a target bit rate. Our model depends on video activity measures that can be easily calculated from the uncompressed video. In our experiments, the model achieves a Pearson correlation of 0.99 and a root-mean-square error of less than 5% with the measured bit rate values, as verified by statistical analysis.

The focus of the present thesis is the uplink delivery of live video using dynamic adaptive streaming over HTTP (DASH). This thesis is specifically concerned with live uplink streaming of automotive videos from the front-facing camera of a vehicle over rate-constrained heterogeneous radio access networks (RANs). DASH splits an input video in small segments each containing a few seconds of playback time and requires each segment to be available in multiple bit rates (video levels). However, the process of simultaneously creating video levels at different bit rates is computationally demanding and quickly exceeds the computational capacity of electronic control units (ECUs) of vehicles. The major objective of this thesis is to design, implement and evaluate encoder side video level selection approaches to reduce the number of video levels that need to be encoded. Another goal is to maximize the perceptual quality of the encoded video segments by selecting the quality-optimal spatial and temporal video encoding parameters based on an objective video quality metric (VQM). For evaluation of the developed encoder side video level selection algorithms, a reference automotive DASH scenario was created. Firstly, TCP throughput measurements were performed in vehicular mobility scenarios to characterize the network performance of the considered heterogeneous RANs. The obtained network traces were used to emulate the TCP throughput during the streaming session. Performances of three standard client adaptation algorithms in terms of user quality of experience (QoE) were investigated assuming that a static full set of 12 pre-defined video levels is available for the adaptation process. Secondly, three encoder side video level selection algorithms, which pre-filter the static full video level set according to network or client context information, were developed. To this end, two of the proposed algorithms use the measured TCP throughput information whereas the third algorithm uses the HTTP request history of the client. Performances of the proposed encoder side video level selection algorithms were compared to a reference implementation which considers the static full set of the 12 pre-defined video levels. Results show that the proposed video level selection algorithms lead to a reduction of 67%– 83% in the number of video levels that need to be encoded. At the same time, a similar QoE is achieved in terms of mean subjective quality of the delivered video segments, interrupted playback duration due to playout buffer underruns, and number of quality switches when compared to the reference implementation. These results suggest that through dynamic encoding of video levels at the server side, DASH can be successfully deployed in resourceconstrained vehicular environments for uplink video delivery.

In the near term, vehicles will be connected with each other and to centralized off-board servers to provide automotive services. In this paper we analyze existing heterogeneous network selection algorithms. It is shown that the developed algorithm for network selection with additional cooperative information is well suited to improve the handover decisions compared to reference handover algorithms without additional cooperative information. The reference algorithms include a received signal strength (RSS), a Fuzzy logic- and an analytic hierarchy process (AHP) based handover decision algorithm excluding supplemental cooperative information. A detailed, simulation based, analysis of the perceived quality of service (QoS) and a cost optimization has been performed for the developed vertical handover decision algorithm (VHDA) using AHP with cooperative information.

This paper presents the research and development activities within ”SolarMesh - Energy-Efficient, Autonomous, Wide-Area Wireless Voice and Data Network”, a R&D project funded by the German Federal Ministry of Education and Research. The project, bringing together expertise from academia and industry in Germany, is specifically dedicated to develop reliable wireless communications infrastructure for rural areas in developing countries, such as in sub-Saharan Africa. Wireless mesh networks based on IEEE 802.11 Wireless LAN technology combined with intelligent functions for self-configuration and self-adaptation can provide affordable ICT infrastructure for access and backhaul operation while at the same time offering carrier-grade QoS for voice and data services. Moreover, the paper outlines how the SolarMesh network operates independently from (potentially unreliable) local energy grids using autarkic energy supply (solar power) and implementing energy-aware routing and handover functions.

The new cellular communication standard 3GPP Long Term Evolution (LTE) promises high throughputs and low latencies, thus enabling even more bandwidth-demanding and real-time critical services for end-users. This is of particular interest for vehicle manufacturers who in the future intend to offer a huge variety of cooperative driver assistance services with different quality of service (QoS) settings. In this paper we analyze the suitability of LTE for future automotive off-board services in terms of transmission delays and reliability under various QoS settings. Our investigations are based on extensive LTE system-level simulations under different load conditions and network deployments as well as on a theoretical delay analysis. The results show that an accurate selection of the LTE QoS parameters is crucial in order to meet the delay and reliability requirements of future automotive applications, especially in high-load network conditions.

This paper presents a novel approach for cyber-physical network control. "Cyber-physical" refers to the inclusion of different parameters and information sources, ranging from physical sensors (e.g. energy, temperature, light) to conventional network information (bandwidth, delay, jitter, etc.) to logical data providers (inference systems, user profiles, spectrum usage databases). For a consistent processing, collected data is represented in a uniform way, analyzed, and provided to dedicated network management functions and network services, both internally and, through an according API, to third party services. Specifically, in this work, we outline the design of sophisticated energy management functionalities for a hybrid wireless mesh network (WLAN for both backhaul traffic and access, GSM for access only), disposing of autonomous energy supply, in this case solar power. Energy consumption is optimized under the presumption of fluctuating power availability and considerable storage constraints, thus influencing, among others, handover and routing decisions. Moreover, advanced situation-aware auto-configuration and self-adaptation mechanisms are introduced for an autonomous operation of the network. The overall objective is to deploy a robust wireless access and backbone infrastructure with minimal operational cost and effective, cyber-physical control mechanisms, especially dedicated for rural or developing regions.

Emergency situations where lives are at stake, such as natural disasters, accidents, or serious fire, require low reaction times. Especially in sparsely populated areas the distance between the nearest emergency station and its disaster location may be quite large. In order to minimize reaction times, emergency communication is to be prioritized with respect to other network services. For enabling efficient and privileged usage of available network resources for emergency services, we propose to handle diverse service requests, ranging from emergency voice calls to bandwidth-consuming streaming services for emergency news, collaboratively by a Joint Call Admision Control (JCAC) and Dynamic Bandwidth Adaptation (DBA) approach. Therefore, we introduce a novel utility definition of services. It represents a generic measurement of the provided level of importance of the emergency service with respect to the common utility, i.e. the utility of service provisioning for the population. The designed JCAC and DBA algorithms cooperatively manage resources of heterogeneous wireless networks and aim at supporting a maximum number of requested services. Further, system utilization is optimized by improving the QoS characteristics of the already granted, elastic services. Simulation results show an improvement in the overall gained utility for emergency services compared to other research approaches.

Due to the fast evolution of mobile devices, a high penetration of notebooks and smart-phones, and the growing demand for high rate services, e.g. mobile video and P2P services, modern radio access networks require efficient means for call admission control and radio resource management. In particular, network operators, managing co-deployed, heterogeneous access technologies, are interested in an optimized utilization of their infrastructure while maximizing their revenue. For ensuring maximum operator gain and optimized system performance, we propose to handle diverse user service requests, ranging from voice only to bandwidth-consuming streaming services, collaboratively by a combined, heuristic Joint Call Admission Control (JCAC) and Dynamic Bandwidth Adaptation (DBA) approach. This approach aims at maximizing overall system utilization and, hence, the mobile network operator's revenues, while keeping the blocking and dropping rates at acceptably low levels, and ensuring that Quality of Service (QoS) demands of the diverse services are met. Therefore, we introduce a novel utility definition of services which is used in the proposed algorithms. It represents a generic measurement of the profit that is gained by the mobile network operator. The JCAC and DBA algorithms are realized tightly coupled and ensure that a maximum number of requested services can be supported by the cooperatively managed, wireless systems in the considered service area. Further, system utilization is optimized by improving the QoS characteristics of the already granted elastic services. In order to evaluate the algorithms for a given scenario of co-deployed Long Term Evolution (LTE) and High Speed Packet Access (HSPA) network nodes, an event-driven simulation platform based on OMNeT++ has been developed, including all relevant entities of 3GPP's SAE. The results show an improvement in the overall gained utility of the mobile network operator compared to standard approaches, and optimized- - system performance w.r.t. utilization as well as acceptable low blocking and dropping rates.