In the dawn of the 6G era, edge computing environments have to cope with the increasing demands of nextgeneration cloud-native applications, such as time-sensitive applications. In this context, the application-graph placement problem is further exacerbated by the need to efficiently schedule timesensitive traffic in order to meet stringent latency or other Quality of Service (QoS) requirements. This time-sensitive aspect, which is often overlooked, raises the need for a unified placement and scheduling approach. To this end, we propose a holistic approach to the placement and scheduling problem for time-sensitive applications within edge computing facilities. In particular, we employ Constraint Programming (CP) to compute efficient solutions in a single step, avoiding the limitations stemming from the sequential execution of separate placement and scheduling solvers. Our solution is compliant with industrial Time-Sensitive Networking (TSN) standards (i.e., IEEE 802.1 Qbv). A comparative evaluation among a range of CP variants sheds lights into various aspects, such as the gains stemming from the use of search heuristics.

In-band network telemetry (INT), enabled by programmable data planes and the appearance of programming protocol-independent languages such as P4, emerged as a viable approach for network monitoring. INT allows the collection of fine-grained network information in real-time, increasing network visibility, at the cost of network overhead. Several lightweight INT approaches have been recently proposed that attempt to alleviate the transmission overhead of INT, while maintaining a high degree of monitoring accuracy. However, their impact on the resources of the respective hardware network devices has been hardly investigated as most of the approaches are evaluated via simulation. In this study, we provide proof of concept implementations of two lightweight INT approaches that have been proposed for path tracing on the Intel Tofino ASIC, identifying the challenges of porting the solution to the selected target. We examine their performance, providing an in-depth analysis of resource consumption.

In-band network telemetry (INT), empowered by programmable dataplanes such as P4, comprises a viable approach to network monitoring and telemetry analysis. However, P4-INT as well as other existing frameworks for INT yield a substantial transmission overhead, which grows linearly with the number of hops and the number of telemetry values. To address this issue, we present a deterministic and a probabilistic technique for lightweight INT, termed as DLINT and PLINT, respectively. In particular, DLINT exercises per-flow aggregation by spreading the telemetry values across the packets of a flow. DLINT relies on switch coordination through the use of per-flow telemetry states, maintained within P4 switches. Furthermore, DLINT utilizes Bloom Filters (BF) in order to compress the state lookup tables within P4 switches. On the other hand, PLINT employs a probabilistic approach based on reservoir sampling. PLINT essentially empowers every INT node to insert telemetry values with equal probability within each packet. Our evaluation results corroborate that both proposed techniques alleviate the transmission overhead of P4-INT, while maintaining a high degree of monitoring accuracy. In addition, we perform a comparative evaluation between DLINT and PLINT. DLINT is more effective in conveying path traces to the telemetry server, whereas PLINT detects more promptly path updates exploiting its more efficient INT header space utilization.

Network slicing has been at the forefront of 5G network research, with various slicing orchestration architectures seeking to reap the benefits of slicing for the enhanced performance and reliability of 5G (and beyond) network services. In this context, cross-slice communication (CSC) has drawn significant attention, since CSC can foster interactions among services deployed in co-located slices, lowering the barrier for the consumption of services.To capitalize the benefits of CSC (e.g., reduced latency and cost), CSC should be established with the highest degree of co-location and also in a secure and policy-compliant manner. To this end, we present an orchestration framework that fulfills all main technical requirements for CSC instantiation. In this respect, we elaborate on the CSC instantiation workflows and shed light into the cross-layer interactions that span our proposed CSC orchestrator, the Network Function Virtualization Orchestrator (NFVO) and the Virtualized Infrastructure Manager (VIM). Our experimental results indicate that our proposed CSC orchestration framework introduces a negligible performance overhead and also incurs a minimal latency inflation compared to a direct form of inter-slice communication without any provision for security and resource isolation.

Resource optimization arguably comprises a crucial aspect for Network Function Virtualization (NFV) infrastructures. In this respect, the problem of virtualized network function (VNF) placement commonly entails the selection of the most appropriate server within a single or among multiple Points-of-Presence (PoPs). Nevertheless, CPU cache hierarchy and memory locality in NUMA multi-core servers along with the diversity in NFV resource profiles introduce significant challenges in terms of intra-server resource allocation; a problem that is often overlooked. As such, we stress on the need for fine-grained resource allocation in NFV infrastructures, and, to this end, we study various aspects of CPU allocation for VNF chains. We deem this intra-server resource allocation problem as complementary to the large body of literature that seeks to optimize VNF placement onto virtualized infrastructures. More particularly, we shed light on the intra-server VNF placement problem, treating CPU cores as the main resource allocation unit. To this end, we assess the performance of multiple CPU allocation combinations under varying server utilization levels and processing workloads with diverse requirements in terms of CPU and memory. Our experimentation approach lets us progressively gain useful insights, which ultimately form ground rules that can be leveraged for optimized server resource allocation.

Network Function Virtualization (NFV) opens us great opportunities for network processing with higher resource efficiency and flexibility. Nevertheless, intelligent orchestration mechanisms are required, such that NFV can exploit its potential and fill up to its promise. In this respect, we investigate the potential gains of embracing Artificial Intelligence (AI) for the virtual network function (VNF) placement problem. To this end, we design and evaluate a genetic algorithm, which seeks efficient embeddings with runtimes on par with heuristic methods. Our proposed embedding method exhibits innovations in terms of network representation and algorithm design, thereby, deviating from typical genetic algorithms. Compared to a heuristic, the proposed genetic algorithm yields higher request acceptance rates, stemming from more efficient resource utilization. We further study a range of factors and parameters that affect the efficiency of the genetic algorithm.

We propose a Software-Defined Network (SDN)based approach for ultra-fast recovery of Time Sensitive Networks (TSN) in the case of failure events. We exploit the Source Routing paradigm for explicit path control and the creation of a stateless TSN data plane. We further propose a TSN failure recovery routing heuristic used to minimise link congestion, while we also introduce the concept of TSN subgraphs to quickly reschedule the flows traversing the problematic area. We evaluate our approach using SDN-based Source Routing and Linux-based TSN scheduling integrated into Mininet.

Service Function Chain (SFC) embedding optimization is crucial for the resource efficiency of Network Function Virtualization infrastructures (NFVI). Nevertheless, high utilization and/or fragmentation levels of a NFVI can significantly restrict the feasible solution space of any SFC embedding method, leading to inefficient SFC placements, or even inhibit SFC embedding. To rectify this problem, we stress on the need for SFC graph transformation (SFC-GT), i.e., explore the potential of SFC graph expansion prior to its embedding. SFC-GT aims at decomposing virtualized network functions (VNFs) into multiple instances with lower resource demands, facilitating their placement onto the NFVI. In this respect, we discuss the trade-off between embedding flexibility and complexity, in the context of SFC-GT. We formulate SFC-GT as a multi-objective optimization problem and design a mixed-integer linear program (MILP) to tackle it. Our simulation results demonstrate notable resource efficiency gains when SFC-GT is utilized prior to SFC embedding.

Network Function Virtualization (NFV) poses the need for efficient embeddings of network services, usually defined in the form of service graphs, associated with resource and bandwidth demands. As the scope of NFV has been expanded in order to meet the requirements of virtualized cellular networks and emerging 5G services, the diversity of resource demands across dimensions, such as CPU, memory, and storage, increased. This requirement exacerbates the already challenging problem of network service embedding (NSE), rendering most existing NSE methods inefficient, as they commonly account for a single resource dimension (i.e., typically, the CPU). In this context, we investigate methods for NSE optimization across multiple resource dimensions. To this end, we study a range of multi-dimensional mapping efficiency metrics and assess their suitability for heuristic and exact NSE methods. Utilizing the most suitable and efficient metrics, we propose two heuristics and a mixed integer linear program (MILP) for optimized multi-dimensional NSE. In addition, we devise a virtual network function (VNF) bundling scheme that generates (resource-wise) balanced VNF bundles in order to augment VNF placement. Our evaluation results indicate notable resource efficiency gains of the proposed heuristics compared to a single-dimensional counterpart, as well as a minor degree of sub-optimality in relation to our proposed MILP. We further demonstrate how the bundling scheme affects the embedding efficiency, when coupled with our most efficient heuristic. Our study also uncovers interesting insights and potential implications from the utilization of multi-dimensional metrics within NSE methods.

In the era of 5G networks, the demand for high quality service provisioning is growing extremely fast. The enabling of Network Function Virtualization and Network Slicing in the scope of 5G network aims to meet the strict requirements of various business cases. Alongside, the complexity of deployment such services becomes also higher, regarding the differences between infrastructure capabilities and the plethora of various individual requirements. This fact makes the selection of the appropriate infrastructure for slice deployment a complex, but also, a major process, as the optimization of the selection leads to the satisfaction of the user and the better resource allocation from the provider's perspective. In this work, an Edge PoP Selection framework for network slice deployment is proposed. This framework takes into account the user's hard and soft requirements and performs a two-stage selection. The selection of the appropriate infrastructure is based on a multi-criteria decision making method. The proposed framework is evaluated and compared with simple filtering and single-objective selection approaches. The promising results show the importance of the two stage framework in order to simultaneously meet the user's requirements and the optimal utilization of the resources.

The increasing resource demand and diversity of network services is taken under serious consideration by the various stakeholders, driving the architecture design of 5G (and beyond) networks. Network slicing, as a prominent aspect of next-generation network architectures, aims at satisfying the diverse service requirements in terms of throughput, latency, reliability, and/or security. However, the prevailing way of slice provisioning, i.e., in the form of isolated bundles of computing, storage, and network resources, makes cross-slice communication inefficient, especially at the network edge. This inevitably hinders opportunities for Business-to-Business (B2B) synergies at the event of service co-location. In this paper, we study this novel aspect of network slicing, i.e., cross-slice communication (CSC). We particularly promote a form of optimized CSC, at which two co-located slices can establish peering in a secure and controlled manner, by confining peering traffic within the boundaries of the datacenter, while still preserving the important aspect of resource isolation. Such optimized CSC can foster synergies between service providers without additional latency or traffic in the backhaul/transport network. In this context, we investigate various ways to establish optimized CSC at edge computing infrastructures, based on functionalities offered by state-of-the-art management and orchestration (MANO) frameworks, such as OpenSourceMANO.

The evolution of Internet of Things (IoT) has sparked significant research interest in edge computing. Within this scope and given the ever-increasing number of IoT and mobile devices, computation offloading is emerging as a cutting-edge and significant research area with enormous potential and practical applications.In this respect, we present the architecture design and experimental evaluation of an orchestration framework for smart computation offloading from IoT or mobile devices to edge cloud servers. The proposed orchestration platform, namely COSMOS, includes control-plane components for workload prediction, load balancing, and admission control. COSMOS is particularly tailored to the needs of an object identification service that receives images from a multitude of Points of Interest (PoIs), performs object identification using a trained model (based on Tensorflow), calculates the prediction accuracy, and finally returns to the end-users the identification outcome and accuracy along with useful information about the identified object. COSMOS has been deployed and evaluated in a large-scale experimental facility that employs OpenStack and OpenSourceMANO (OSM) for Network Function Virtualization (NFV) orchestration. Our experimental results indicate the feasibility of computation offloading for this object identification service and further uncover useful insights in terms of performance and scalability.

The wide adoption of cloud computing, along with the advent of Network Function Virtualization (NFV) and its auspicious applications, have generated a new class of combinatorial optimization problems, with network service embedding (NSE) being one of the most prominent. NSE methods aim at improved resource efficiency and increased revenues for cloud resource providers. However, these methods commonly handle resources types with a single dimension (e.g., virtual nodes with only computing demands), thus limiting the scope of the generated solutions.In an attempt to address NSE under a pragmatic scope, we investigate the potential gains of a heuristic algorithm, which takes into account both the CPU and the memory dimension of virtual nodes. To the best of our knowledge, the novelty of our work lies on the fact that the proposed heuristic exploits insights from research on multi-dimensional virtual machine allocation, i.e., the computation and accounting of a suitability metric across multiple resource dimensions. Our simulation results demonstrate that the proposed NSE method outperforms both a mixed integer linear program (MILP) and a similar heuristic, which do not account for multiple resource dimensions.

Dynamic end-to-end cloud network slices following a Slice-as-a-Service (SaaS) paradigm offer promising opportunities in support of diverse vertical industries attached to different administrative domains. This paper presents a distributed Marketplace model to dynamically create cloud network slices involving different slice resource providers. Portrayed through the lens of three reference use case scenarios, we discuss potential attributes of having a Marketplace and the outcomes feeding the use case requirements within a Slice Marketplace scope. Effective selection, negotiation, and proper monitoring and enforcement of service level agreements between multiple administrative and technological domains are among the prime challenges towards the realization of a distributed Marketplace of federated network and cloud resource providers.

Cloud network slicing can be defined as the process that enables isolated end-to-end and on-demand networking abstractions, which: (a) contain both cloud and network resources, and (b) are independently controlled, managed and orchestrated. This paper contributes to the vision of the NECOS project and relevant platform, that aim to address the limitations of current cloud computing infrastructures to accomplish the challenging requirements of the slicing approach. The NECOS platform implements the Slice-as-a-Service model, enabling the dynamic creation of end-to-end (E2E) slices from a set of constituent slice parts contributed from multiple domains. A challenging issue is to define the facility that implements dynamic slice resource discovery, aligned to the requirements of the slice owner or tenant, over different infrastructure providers. Here, we propose a Marketplace-based approach implementing relevant federated interactions for the resource discovery and we detail its architecture, workflows, and information model. We also present its initial implementation details and provide both quantitative and qualitative experimental results validating its main operation.

Increasing over-the-top video consumption endangers the sustainability of content delivery over the Internet. Internet Service Providers (ISP) face difficulties in competing on value-added services with content providers and Content Delivery Network (CDN) operators. In this respect, we propose a new model for the collaboration between content delivery stakeholders, so that CDN operators can deploy their software in ISP infrastructures leveraging on Network Function Virtualization (NFV). As the ISP network topology and utilization is deemed confidential, we use a high-level Service Level Agreement (SLA) for the negotiation of both computing resources and connectivity, allowing the ISP to optimize server selection, while providing at the same time sufficient flexibility to the CDN operators for content delivery. Furthermore, we present a linear programming formulation for the VNF Service Chain Embedding and an heuristic to increase problem tractability with a small cost overhead. Finally, we validate the efficiency of the proposed service chain model for virtual CDN management.

Network function (NF) virtualization decouples NFs from the underlying middlebox hardware and promotes their deployment on virtualized network infrastructures. This essentially paves the way for the migration of NFs into clouds (i.e., NF-as-a-Service), achieving a drastic reduction of middlebox investment and operational costs for enterprises. In this context, service chains (expressing middlebox policies in the enterprise network) should be mapped onto datacenter networks, ensuring correctness, resource efficiency, as well as compliance with the provider's policy. The network service embedding (NSE) problem is further exacerbated by two challenging aspects: 1) traffic scaling caused by certain NFs (e.g., caches and WAN optimizers) and 2) NF location dependencies. Traffic scaling requires resource reservations different from the ones specified in the service chain, whereas NF location dependencies, in conjunction with the limited geographic footprint of NF providers (NFPs), raise the need for NSE across multiple NFPs. In this paper, we present a holistic solution to the multi-provider NSE problem. We decompose NSE into: 1) NF-graph partitioning performed by a centralized coordinator and 2) NF-subgraph mapping onto datacenter networks. We present linear programming formulations to derive near-optimal solutions for both problems. We address the challenging aspect of traffic scaling by introducing a new service model that supports demand transformations. We also define topology abstractions for NF-graph partitioning. Furthermore, we discuss the steps required to embed service chains across multiple NFPs, using our NSE orchestrator (Nestor). We perform an evaluation study of multi-provider NSE with emphasis on NF-graph partitioning optimizations tailored to the client and NFPs. Our evaluation results further uncover significant savings in terms of service cost and resource consumption due to the demand transformations.

One of the primary challenges associated with network functions virtualization (NFV) is the automated management of the service lifecycle. In this paper, we present a full software-based management and orchestration (MANO) stack which operates with OpenStack and OpenDaylight controllers and has the in-built functionality to automate the key phases of the NFV service lifecycle, namely resource discovery and matching, service mapping, service deployment, and monitoring. The MANO stack is being implemented by the EU FP7 project T-NOVA, with the components being released as open-source software. Service mapping and service deployment solutions developed in the scope of T-NOVA are presented in detail. As a proof-of-concept, we evaluate the performance of a virtualized traffic classifier network function, demonstrating the gains of virtualized hardware acceleration.

The connection-oriented nature of wireless access has been setting limitations to accessing Internet services. In particular, Internet access via public Wi-Fi infrastructure typically requires a subscription and a complicated authentication procedure that can hinder connection establishment, according to recent reports. In this respect, we present FreeSurf, an architecture for turning existing wireless access infrastructure into an application-centric one. FreeSurf essentially enables users to authenticate to wireless networks using their Service Provider (SP) accounts and further permits connections towards the SP's domain. The delegation of authentication and policy based access control leverages on software-defined networking principles. We discuss the FreeSurf architecture, enabling technologies, and incentives for its adoption. We further use a prototype implementation to assess FreeSurf in terms of authentication delay and flow table lookup performance.

The migration of network functions (NFs) into virtualized network infrastructures brings significant benefits to enterprise networks, while creating opportunities for new cloud service models (i.e., NF-as-a-Service). Network service embedding (NSE) entails serious challenges, stemming from middle-box policies prescribed by network operators and the implications of NFs on network traffic (i.e., bandwidth conservation or traffic amplification) that complicate the estimation of bandwidth demands. The NSE problem is further exacerbated by the location dependencies of certain NFs, which, in conjunction with the limited geographic footprint of NF providers, raise the need for network service mapping across multiple providers. In this paper, we present a holistic approach to multi-provider NSE. We introduce a new service model that simplifies the specification of network service requests and the estimation of bandwidth demands. We further define topology abstractions tailored to NSE that are exposed to a network service composition layer (NSCL), interposed between the clients and the NF providers. Based on this service model and topology abstractions, we propose Nestor, a system that generates efficient network service embeddings via network graph rendering, request partitioning among datacenters (DCs), and request segment mappings onto DC networks.

Security and liability issues set barriers to WiFi sharing, giving away opportunities to capitalize the unused capacity in WiFi networks and provide wider Internet access. Considering the lack of trust between WiFi sharers and potential guests, we leverage on the increasing penetration of online social networks to enable WiFi sharing with online friends. To this end, we present a WiFi sharing architecture, called Social WiFi, and the associated mechanisms for network discovery and authentication. Social WiFi couples Bloom Filter with extensions to existing authentication mechanisms for the discovery and authentication of guests to WiFi networks owned by their online friends. We assess the authentication in Social WiFi in terms of performance and efficiency. Furthermore, we discuss deployment issues and lay out a delegation framework for the migration of the existing WiFi network infrastructure to Social WiFi.

No abstract available.

The ever-increasing need to diversify the Internet has recently revived the interest in network virtualization. Wide-area virtual network (VN) deployment raises the need for VN embedding (VNE) across multiple Infrastructure Providers (InPs), due to the InP's limited geographic footprint. Multi-provider VNE, in turn, requires a layer of indirection, interposed between the Service Providers and the InPs. Such brokers, usually known as VN Providers, are expected to have very limited knowledge of the physical infrastructure, since InPs will not be willing to disclose detailed information about their network topology and resource availability to third parties. Such information disclosure policies entail significant implications on resource discovery and allocation. In this paper, we study the challenging problem of multi-provider VNE with limited information disclosure (LID). In this context, we initially investigate the visibility of VN Providers on substrate network resources and question the suitability of topology-based requests for VNE. Subsequently, we present linear programming formulations for: (i) the partitioning of traffic matrix based VN requests into segments mappable to InPs, and (ii) the mapping of VN segments into substrate network topologies. VN request partitioning is carried out under LID, i.e., VN Providers access only information which is not deemed confidential by InPs. We further investigate the suboptimality of LID on VNE against a “best-case” scenario where the complete network topology and resource availability information is available to VN Providers.

Virtual network embedding algorithms that optimize the mapping of virtual network (VN) topologies onto a substrate network usually do not comply with the policies of substrate providers which may prefer to identify and embed the most profitable subset of a VN request. Such policy-based VN embedding (VNE) is required by distributed VNE architectures, such as PolyVine, or by auction-based VNE environments. In this paper, we introduce a policy dimension to VNE by proposing a new VNE algorithm that aims at maximizing the revenue without violating the provider's policy. In contrast to the greedy nature of most VNE techniques, our algorithm allows a provider to trade short-term revenue gains for higher revenue in the long term and cope better with evolving demands. Our simulation results corroborate the efficiency of our VNE algorithm and show the impact of diverse policy adjustments on resource utilization and generated revenue.

Software-defined networking (SDN) has emerged to circumvent the difficulty of introducing new functionality into the network. The widespread adoption of SDN technologies, such as OpenFlow, can facilitate the deployment of novel network functions and new services. Network infrastructure providers can significantly benefit from the SDN paradigm by leasing network slices with SDN support to Service Providers and end-users. Currently, the deployment of arbitrary virtual SDN topologies entails significant configuration overhead for SDN operators. To this end, we present a SDN virtualization layer that orchestrates the deployment and management of virtual SDNs (vSDN). The so-called SDN hypervisor generates and installs the forwarding entries required for vSDN setup and also coordinates the necessary switch flow table modifications for seamless resource migration. Furthermore, the hypervisor transparently rewrites all control messages enforcing flowspace isolation while giving to the vSDN operator the illusion of exclusive access control. We explore the design space and prerequisites for SDN virtualization, including the selection and encoding of packet identifiers, the resolution of flowspace identifiers, and the configuration and consolidation of multiple virtual flow tables onto a single switch in order to provide support for arbitrary topologies. Furthermore, we discuss the scalability of the SDN control and data plane.

Universal access to Internet is crucial, and as such, there have been several initiatives to enable wider access to the Internet. Public AccessWiFi Service (PAWS) is one such initiative that takes advantage of the the available unused capacity in home broadband connections and allows Less-than-Best Effort (LBE) access to these resources, as advocated by Lowest Cost Denominator Networking (LCDNet). PAWS has been recently deployed in a deprived community in Nottingham, and, as any crowd-shared network, it faces limited coverage, since there is a single point of Internet access per guest user, whose availability depends on user sharing policies. To mitigate this problem and extend the coverage, we consider a crowd-shared wireless mesh network (WMN) in which the home routers are interconnected as a mesh. Such a network provides multiple points of Internet access and can enable resource pooling across all available paths to the Internet backhaul. In this paper, we investigate the potential benefits of a crowd-shared WMN for public Internet access by performing a comparative study between such a network and PAWS. To this end, we present a software-defined WMN control plane for the coordination of traffic redirections through the WMN and an algorithm for Internet access point selection. Our simulation results show that a crowd-shared WMN can provide much higher utilization of the shared bandwidth and can accommodate a substantially larger volume of guest user traffic.

In recent years network virtualization has emerged as an essential instrument for spurring innovation and consolidating resources. As a key technology, router virtualization enables the operation of multiple logical router instances within a single box. However, both commercial hardware solutions and PC-based software prototypes exhibit limitations in terms of programmability, memory, forwarding performance or port density. In this paper, we present OpenVRoute, an architecture that satisfies the requirements for router virtualization by combining the advantages of commodity switches and server hardware while mitigating their inherent shortcomings. Open-VRoute provides a transparent binding between the logical and physical router resources, using OpenFlow as a glue between the individual architecture components. OpenVRoute employs a split forwarding plane, caching high data-rate flows in an external OpenFlow switch while processing low-volume traffic in a software datapath hosted on a server. We exemplify the OpenVRoute architecture, and evaluate the performance and scalability of the main components.

We present AutoEmbed, a fully-automated framework for VN embedding across multiple substrate networks. To automate VN embedding, AutoEmbed deploys functions over three layers: (i) Service Providers, (ii) VN Providers, and (iii) Infrastructure Providers (InPs). AutoEmbed enables VN Providers to partition VN requests among multiple substrate networks based on resource and network topology information that is not treated as confidential by InPs.

This paper presents a platform for virtual network (VN) provisioning across multiple domains. The platform decomposes VN provisioning into multiple steps to address the implications of limited information disclosure on resource discovery and allocation. A new VN embedding algorithm with simultaneous node and link mapping allows to assign resources within each domain. For inter-domain virtual link setup, we design and realize a signaling protocol that also integrates resource reservations for providing virtual links with Quality-of-Service guarantees. Experimental results show that small VNs can be provisioned within a few seconds.

Limited bandwidth and variable network conditions compromise throughput performance and smoothness, and eventually impair the playback quality of video streams. Scalable video coding provides enhanced flexibility in stream manipulation, adapting video to the network dynamics. The efficiency of a scalability technique is dependent on the underlying congestion control and particularly on its ability to maintain a smooth transmission rate. In this context, we combine a smooth transmission control, namely scalable streaming video protocol (SSVP), with a temporally coding algorithm in order to optimize the delivery and playback of MPEG-4 streaming video. Simulation experiments indicate that this combined approach can provide significant performance gains over a wide range of network and session dynamics.

In this paper, we propose a new streaming protocol, namely dynamic video rate control (DVRC), which enables adaptive video delivery over the Internet. DVRC operates on top of HDP providing a congestion-controlled flow of unreliable datagrams. The proposed rate control scheme is able to interact with new and existing video streaming applications which are capable of adjusting their rate based on congestion feedback. DVRC attempts to optimize the performance of video delivery with concern to friendliness with interfering traffic. Exploring DVRC's potential through extensive simulations, we identify notable gains in terms of bandwidth utilization and smooth video delivery. Furthermore, our results indicate that the protocol allocates a well-balanced amount of network resources maintaining friendliness with coexisting flows.

Media-streaming applications experience limited performance and perceptible quality degradation in the presence of random wireless errors, as the underlying congestion control typically interprets packet loss as the outcome of congestion. In this context, we propose a selective rate control, namely AIAMD, which manages to differentiate congestive and non-congestive loss by utilizing history in its control rules. AIAMD combines the most desirable features of additive increase additive decrease (AIAD) and additive increase multiplicative decrease (AIMD) controls, reacting gently to wireless loss and more aggressively to congestion. Exploring AIAMD's potential, we identify notable gains in terms of link utilization and media delivery, without compromising intra-protocol fairness.

We evaluate selected research proposals towards the efficient real-time QoS management over wireless links. We mainly focus on real-time performance of link-and transport-level mechanisms that bind operationally wired and wireless links. Employing a new metric for the evaluation of real-time performance we demonstrate that there are occasions where increased goodput does not correspond to real-time performance gains. In the sequel, we exploit further the potential of transport layer approaches

In this paper, we propose a new transport protocol, namely scalable streaming video protocol (SSVP), which employs an AIMD-oriented congestion control mechanism. SSVP, in a complementary role, operates on top of UDP and is specifically designed to support unicast video streaming applications. The transmission rate is controlled by properly adjusting the inter- packet-gap, spacing outgoing packets evenly to produce a smoothed flow. SSVP attempts to optimize the performance of streaming video delivery with concern to friendliness with interfering traffic. Quantifying SSVP's performance, we identify that the protocol utilizes a higher fraction of the available bandwidth, and maintains a regular transmission rate with oscillations of a smaller magnitude in comparison with existing congestion control schemes.

We study transport protocol performance from an application-specific perspective. Initially, we focus on TCP and UDP supportive role in the context of VoIP performance. Applying our metric for real-time performance, we discover that UDP has limited efficiency. Beyond UDP/TCP, we evaluate a solution-framework based on TCP protocols which incorporate variable congestion mechanisms. We also investigate VoIP traffic friendliness, as well as potential tradeoffs between protocol performance and fairness. Furthermore, we evaluate VoIP quality and protocol sensitivity versus a range of QoS parameter adjustments.

We evaluate the impact of network, and protocol heterogeneity on real-time application performance. We focus on TCP and UDP supportive role, also in the context of network stability and fairness. We reach several conclusions on the specific impact of wireless links, MPEG traffic friendliness, and TCP version efficiency. Beyond that, we also reach an unexpected result: UDP traffic is occasionally worse than TCP traffic when the right performance metric is used.