Packetized Media Streaming with Comprehensive Exploitation of Feedback Information

This paper addresses the problem of streaming packetized media over a lossy packet network, with sender-driven (re)transmission using acknowledgement feedback. The different transmission scenarios associated to a group of interdependent media data units are abstracted in terms of a finite alphabet of policies, for each single data unit. A rate-distortion optimized markovian framework is proposed, which supports the use of comprehensive feedback information. Contrarily to previous works in rate-distortion optimized streaming, whose transmission policies definitions do not take into account the feedback expected for other data units, our framework considers all the acknowledgment packets in defining the streaming policy of a single data unit. More specifically, the notion of master and slave data unit is introduced, to define dependent streaming policies between media packets; the policy adopted to transmit a slave data unit becomes dependent on the acknowledgments received about its masters. One of the main contributions of our work is to propose a methodology that limits the space of dependent policies for the RD optimized streaming strategy. A number of rules are formulated to select a set of relevant master/slave relationships, defined as the dependencies that are likely to bring RD performance gain in the streaming system. These rules provide a limited complexity solution to the rate-distortion optimized streaming problem, with comprehensive use of feedback information. Based on extensive simulations, we conclude that (i) the proposed set of relevant dependent policies achieves close to optimal performance, while being computationally tractable, and (ii) the benefit of dependent policies is driven by the relative sizes and importance of interdependent data units. Our simulations demonstrate that dependent streaming policies can perform significantly better than independent streaming strategies, especially for cases where some media data units bring a relatively large gain in distortion, in comparison with other data units they depend on for correct decoding. We observe however that the benefit becomes marginal when the gain in distortion per unit of rate decreases along the media decoding dependency path. Since such a trend characterizes most conventional scalable coders, the implementation of dependent policies can reasonably be ruled out in these specific cases

Transport Layer Optimizations for Heterogeneous Wireless Multimedia Networks

The explosive growth of the Internet during the last few years, has been propelled by the TCP/IP protocol suite and the best effort packet forwarding service. However, quality of service (QoS) is far from being a reality especially for multimedia services like video streaming and video conferencing. In the case of wireless and mobile networks, the problem becomes even worse due to the physics of the medium, resulting into further deterioration of the system performance. Goal of this dissertation is the systematic development of comprehensive models that jointly characterize the performance of transport protocols and media delivery in heterogeneous wireless networks. At the core of our novel methodology, is the use of analytical models for driving the design of media transport algorithms, so that the delivery of conversational and non-interactive multimedia data is enhanced in terms of throughput, delay, and jitter. More speciffically, we develop analytical models that characterize the throughput and goodput of the transmission control protocol (TCP) and the transmission friendly rate control (TFRC) protocol, when CBR and VBR multimedia workloads are considered. Subsequently, we enhance the transport protocol models with new parameters that capture the playback buffer performance and the expected video distortion at the receiver. In this way a complete end-to-end model for media streaming is obtained. This model is used as a basis for a new algorithm for rate-distortion optimized mode selection in video streaming appli- cations. As a next step, we extend the developed models for the aforementioned protocols, so that heterogeneous wireless networks can be accommodated. Subsequently, new algorithms are proposed in order to enhance the developed media streaming algorithms when heterogeneous wireless networks are also included. Finally, the aforementioned models and algorithms are extended for the case of concurrent multipath media transport over several hybrid wired/wireless links.Ph.D.Committee Chair: Vijay Madisetti; Committee Member: Raghupathy Sivakumar; Committee Member: Sudhakar Yalamanchili; Committee Member: Umakishore Ramachandran; Committee Member: Yucel Altunbasa

Online learning on the programmable dataplane

This thesis makes the case for managing computer networks with datadriven methods automated statistical inference and control based on measurement data and runtime observations—and argues for their tight integration with programmable dataplane hardware to make management decisions faster and from more precise data. Optimisation, defence, and measurement of networked infrastructure are each challenging tasks in their own right, which are currently dominated by the use of hand-crafted heuristic methods. These become harder to reason about and deploy as networks scale in rates and number of forwarding elements, but their design requires expert knowledge and care around unexpected protocol interactions. This makes tailored, per-deployment or -workload solutions infeasible to develop. Recent advances in machine learning offer capable function approximation and closed-loop control which suit many of these tasks. New, programmable dataplane hardware enables more agility in the network— runtime reprogrammability, precise traffic measurement, and low latency on-path processing. The synthesis of these two developments allows complex decisions to be made on previously unusable state, and made quicker by offloading inference to the network. To justify this argument, I advance the state of the art in data-driven defence of networks, novel dataplane-friendly online reinforcement learning algorithms, and in-network data reduction to allow classification of switchscale data. Each requires co-design aware of the network, and of the failure modes of systems and carried traffic. To make online learning possible in the dataplane, I use fixed-point arithmetic and modify classical (non-neural) approaches to take advantage of the SmartNIC compute model and make use of rich device local state. I show that data-driven solutions still require great care to correctly design, but with the right domain expertise they can improve on pathological cases in DDoS defence, such as protecting legitimate UDP traffic. In-network aggregation to histograms is shown to enable accurate classification from fine temporal effects, and allows hosts to scale such classification to far larger flow counts and traffic volume. Moving reinforcement learning to the dataplane is shown to offer substantial benefits to stateaction latency and online learning throughput versus host machines; allowing policies to react faster to fine-grained network events. The dataplane environment is key in making reactive online learning feasible—to port further algorithms and learnt functions, I collate and analyse the strengths of current and future hardware designs, as well as individual algorithms

Prescient R-D Optimized Packet Dependency Management for Low-Latency Video Streaming

We present an improved, prescient, scheme to deliver pre-encoded video streams at very low latency. We dynamically manage the prediction dependency for a group of packets using a ratedistortion (R-D) optimization framework that adapts to the channel, so that the expected end-to-end distortion is minimized under a given rate constraint. In our system, the R-D characteristics of each prediction mode are pre-computed at compression time and stored in an R-D preamble along with the multiple compressed versions of the stream. The distortion in the preamble is estimated using an accurate loss-distortion model, and the optimal prediction modes are computed with an iterative descent algorithm. Complexity is reduced at streaming time by using the preamble to compute the optimal modes instead of actually compressing the video. Experiments demonstrate that the proposed prescient scheme provides significant performance gains over simple Intra-insertion, and consistent bitrate savings compared to the greedy scheme

Big Data Security (Volume 3)

After a short description of the key concepts of big data the book explores on the secrecy and security threats posed especially by cloud based data storage. It delivers conceptual frameworks and models along with case studies of recent technology

Collaborative Sensing in Automotive Scenarios : Enhancement of the Vehicular Electronic Horizon through Collaboratively Sensed Knowledge

Modern vehicles are equipped with a variety of advanced driver assistance systems that increase driving comfort, economy and safety. Respective information sources for these systems are local sensors, like cameras, radar or lidar. However, the next generation of assistant systems will require information above the local sensing range. An extension of the local perception can be provided by the use of appro- priate communication mechanisms. Hence, other vehicles can serve as an informa- tion source by providing their local perception data, but also any other information source, such as cloud services. Required communication can take place directly be- tween vehicles via mobile ad-hoc communication or via a backend by the use of cellu- lar communication. The appropriate technology depends on the respective use case, that determines information content, granularity and tolerated latency. Based on liter- ature, we derived a categorization of use case dependent information demands, with respect to communication. The resulting three zones, namely safety zone, awareness zone and information zone, refer to the tolerated latency between the occurrence of an information and the point in time the information has to be processed at the receiver side. While communication mechanisms for the safety zone, i. e., the ego-vehicle’s di- rect surroundings with a remaining driving time of less than 2 − 5 seconds, have been focus in research and standardization in the past, respective mechanisms for larger distances have not been sufficiently considered. In this thesis, we examine in- formation distribution mechanisms in context of the previously mentioned use case categories. As the first key contribution, we consider the gathering of vehicular sensed data with regard to the information zone, i. e., more than 30 seconds remaining driving time to the point of the information origin. We developed a probabilistic data collection model that is able to reduce data traffic up to 85 % compared to opportunistic trans- mission and still sticks to certain quality metrics, e. g., a maximum detection latency. A central adaption of transmission probabilities to the density of transmitting vehi- cles is applicable for cellular use and copes with sparse traffic situations. Moreover, we have extended this approach by hybrid communication, i. e., the parallel use of cellular and mobile ad-hoc communication. This allows to further reduce cellular based data traffic, in particular in case of dense traffic. As the second key contribution, we examine the efficient distribution of the pre- viously gathered information. Information is structured and prioritized according to the most probable driving path, as so-called electronic horizon. The transmission towards the vehicles is performed in small data packets, according to the given pri- orities. The aim is to transmit only information relevant for road segments that will be used. Concerning this, we developed a mechanism for most probable travel path estimation and a data structure for efficient mapping of the electronic horizon. As the third key contribution, we examine the information exchange in the aware- ness zone, an area between the safety zone and the information zone with about 5 to 30 seconds remaining driving time to the point of the information origin. Derived from the respective use cases, this data is not directly safety relevant, but it is still about dynamic position information of neighboring vehicles. Due to the relatively long distance, direct vehicle to vehicle communication is not possible. Respective data has to be forwarded by intermediate vehicles. However, position beacons with- out data forwarding can already cause channel congestion in dense traffic situations. The use of cellular networks would require absolute total network coverage with permanent free channel resources. To enable forwarding of dynamic vehicle infor- mation anyhow, we developed at first a mechanism to reduce the channel load for position beacons. Next, we use the freed-up bandwidth to forward dynamic informa- tion about neighboring vehicle positions. With this mechanism, we are able to more than double the range of vehicular perception, with respect to moving objects. In extension to standardized communication mechanisms for the safety relevant direct proximity, our three mentioned contributions provide the means to complete the long range vehicular perception for future advanced driver assistance systems