5G networks promise to deliver an unprecedented performance that can accommodate novel services with stringent Quality of Service (QoS) requirements that were not possible with previous generations of networks. Edge Computing plays a fundamental role by providing computing resources closer to the user, reducing round trip times. However, the deployment of edge computing poses new challenges, including the energy footprint of a potentially large number of servers. Even in idle state, these servers consume a significant amount of energy, which is worth considering for reducing their energy footprint. In cloud computing environments, server shutdown during low-demand periods is a typical energy-saving strategy. However, this approach has received less attention in edge computing due to the strict latency requirements of its use cases. This work presents ODESA, an edge server shutdown strategy with polynomial time complexity that provides a tradeoff between the idle energy consumption of the edge servers and energy consumed by the backhaul to route requests to active servers. Our numerical investigation shows that thanks to the reduction in idle energy consumption, ODESA reduces the total consumption by 42% over the common always-on approach during low-demand periods and 11% over 24 hours, all while meeting the latency requirements of the applications.

While advances in wireless networks enable novel services with previously unreachable latency guarantees, edge computing becomes essential for delivering computing resources close to the users and meeting the strict latency requirements. However, addressing the energy footprint of computing resources is crucial amid the pressing sustainability concerns. The energy consumption of idle resources accounts for a significant part of the total energy footprint. While server shutdown during low-demand periods is common in cloud computing, it is challenging to determine which edge servers to shut down and how to route requests due to the stringent latency requirements of the applications. Thus, this work formulates an optimal orchestration policy to minimize the energy consumption of the edge computing infrastructure and presents LESS-ON, a strategy with a polynomial time complexity that reduces the operational energy footprint of edge computing by shutting down edge servers during low-demand periods. In contrast to previous studies, LESS-ON considers the energy requirements associated with routing requests to the designated edge servers. Our numerical evaluation shows that LESS-ON reduces the total consumption by 42% with respect to the common always-on approach during low-demand periods and by 35% over 24 h, all while meeting latency requirements.

Abstract While cloud computing is crucial in processing data from devices with low computational power, the latency introduced by the Internet backhaul limits real-time applications. By situating computing resources at the network’s edge, edge computing offers low-latency services by offloading computations from high-performance computing (HPC) data centers to the edge servers, reducing wide Area network (WAN) strain. As a result, edge computing has unlocked opportunities for innovative applications that were previously unfeasible, such as connected vehicles or medical robotics. Nonetheless, deploying the infrastructure required to support edge computing services raises sustainability and energy consumption concerns. Consequently, the development of tools enabling researchers to explore innovative approaches to reducing the energy impact of edge computing is crucial. In this work, we present MintEDGE, a network simulator focused on the energy consumption of edge computing. Our simulator allows testing energy-saving approaches and task placement algorithms in realistic large-scale scenarios encompassing entire regions.

Edge computing has transformed cellular networks, offering fast response times by moving computing resources to the network's edge. This not only reduces the burden on the Wide Area Network (WAN) but also enables latency-sensitive applications. However, the widespread deployment of edge computing raises concerns regarding its sustainability. In this work, we present MintEDGE, a simulation framework that models a fully configurable edge-enabled cellular network. MintEDGE empowers researchers and practitioners to design and assess energy-saving strategies for edge computing. We discuss the details of the simulator and its customizable elements like user mobility, the possibility to use predictive workload algorithms, and diverse application scenarios at scale. MintEDGE is released under a permissive MIT license.

The COVID-19 emergency has made the consumption of multimedia content skyrocket in all contexts, including education. Many universities leverage hybrid learning models, in which students join a real-time video session via Wi-Fi from several classrooms to ensure safety and social distancing. This is creating a significant strain on the wireless access network, which is required to deliver an unusually high level of traffic. Artificial Intelligence (AI) and Machine Learning (ML) solutions have emerged as a way to make networks easier to control and to manage. However, their black box nature and in general their fire and forget approach has generated considerable skepticism over the entire value chain, from vendors to network administrators. This situation has led to a new interest in interpretable AI solutions, which aim at making the decisions taken by AI/ML models intelligible to a domain expert. In this article, we review the concept of interpretable AI and analyze the challenges, requirements, and benefits it can bring to delay-sensitive content delivery in 802.11 Wi-Fi networks. Furthermore, we apply these requirements to a use case in which we focus on advanced Quality of Service (QoS) provision, and we propose an interpretable and low-complexity ML model that addresses those requirements. The results demonstrate performance gains up to 60% in the sensitive traffic and up to 20% at network-wide level.

Wireless technologies have come to stay, fuelled by the digital world in a global culture that expects instant access to information. The explosive increase in Wi-Fi enabled devices and the incessant demand for high quality multimedia contents require to find innovative ways for accommodating these latency sensitive applications. In this Dissertation we explore multimedia content distribution over IEEE 802.11 networks and tackle the existing difficulties through three main approaches. First, we endeavour to enhance the channel access and the QoS provisioning by relying on machine learning models. Then, we leverage the SDN paradigm to provide efficient radio resource management through adaptive channel assignment and traffic distribution methods. Finally, we propose an integral SDN-based solution to address the shortcomings found in multicast multimedia transmissions in Enterprise WLANs.

Although 4G and 5G Radio Access Technologies(RATs) aim to usher in faster connectivity that is able to cope with mobile traffic demands, this capability is sometimes hindered by poor indoor signal quality caused by distance from base stations and the materials used in the construction of buildings. These factors have led to Wi-Fi being adopted as the technology of choice in indoor scenarios. Although the deployment of Wi-Fi Access Points (APs) can be planned, the user-AP association procedure is not defined by the standard but left to the vendor's choice, which for simplicity is usually driven by signal strength. This approach leads to uneven user distributions and poor resource utilization. To overcome this rigidity, in this paper, we leverage SoftwareDefined Networking (SDN) to propose ajoint user association and channel assignment solution in Wi-Fi networks. Our approach considers average signal strength, channel occupancy, and AP load to make better user association decisions. Experimental results have demonstrated that the proposed solution improves the aggregated goodput by 22% with respect to approaches based on signal strength. Furthermore, user level fairness is also improved.

The increasing demand for live streaming and for remote sensing applications is bringing renewed interest on uplink performances in Wi-Fi networks. Radio diversity can improve the performance of such applications by opportunisti-cally receiving mobile users' traffic at multiple attachment points. However, radio diversity techniques can not be used in standard Wi-Fi networks due to backwards compatibility problems. In this paper we present Wi-Not, a novel SDN-based solution for exploiting radio diversity in software-defined WLANs. Wi-Not allows mobile terminals to be associated to multiple Wi-Fi APs in the uplink direction improving frame delivery probability in uplink-constrained applications. Wi-Not does not require changes to the mobile terminals and can be easily deployed with minimal changes to the network infrastructure. An experimental evaluation carried out over a real-world testbed shows that this approach can deliver an improvement of up to 80% in terms of UDP goodput and up to 60% of TCP throughput. We release the entire implementation including the controller and the data-path under a permissive license for academic use.

In traditional 802.11 networks stations usually try to associate to the AP with the highest signal strength. However, especially in case of very dense deployments, this may lead to uneven wireless clients distribution, and thus to poor network performances. Software Defined Networking (SDN) has recently emerged as a novel approach for network control and management. In this paper we present Wi-Balance, a novel SDN-based solution for joint user association and channel assignment in Wi-Fi networks. An experimental evaluation in a real-world testbed showed that Wi-Balance outperforms the RSSI-based user association schemes in terms of throughput and channel utilization by up to 25% and 30%, respectively. We release the entire implementation including the controller and the data-path under a permissive license for academic use.

Current 802.11-based WLANs are asked to support an ever increasing number of services and applications, each of them characterized by a diverse set of requirements in terms of bitrate, latency, and reliability. Network virtualization and programmability are two emerging trends that can support the realization of such a vision in a cost-effective fashion. In this paper we introduce Lasagna, a novel end-to-end solution that enables flexible management of slices encompassing both the wired and the wireless segments of an Enterprise WLAN. Lasagna allows flexible management of network slices to meet their respective service requirements. An experimental evaluation carried out over a real-world testbed shows that Lasagna can ensure both functional and performance isolation between the different slices and efficient radio resource utilization. We release the entire implementation including the controller and the datapath under a permissive license for academic use.

The ever-increasing demand for mobile content delivery and multimedia services is bringing renewed interest in multicast communications in Wi-Fi based WLANs. Nevertheless, multicast over Wi-Fi raises several challenges including low data rates and coexistence issues with other unicast streams. Some amendments to the Wi-Fi standard, such as 802.11aa, have introduced new delivery schemes for multicast traffic as well as finer control on the low-level aspects of the 802.11 medium access scheme. However, the logic for using such features is left to the implementer of the standard. In this paper, we present SDN@Play Mobile, a novel SDN-based solution for joint mobility management and multicast rate-adaptation in Wi-Fi networks. The solution builds upon a new abstraction, named Transmission Policy, which allows the SDN controller to reconfigure a multicast transmission policy when its optimal operating conditions are not met. An experimental evaluation carried out over a real-world testbed shows that our approach can deliver significant improvements in terms of both throughput and channel utilization compared to the legacy 802.11 multicast scheme. Finally, we release the entire software implementation under a permissive APACHE 2.0 license for academic use.

Wireless network research and development efforts are largely driven by the increasing interest in multimedia applications. Video streaming services, which often involve strict quality of service requirements and are very sensitive to delays, represent a significant proportion of these applications. In IEEE 802.11-based WLANs, these services raise several challenges in terms of robustness, reliability, and scalability, especially when supporting multiple multicast streams at the same time. Nevertheless, traditional network architectures make it difficult to address these problems. In this context, the software defined networking (SDN) paradigm opens new research possibilities by decoupling the control decisions from the data-plane and by improving network management and programmability. In this paper, we present SM-SDN@Play, an SDN-based solution for joint multicast rate selection and group formation in 802.11-based networks. Experimental results show the high performance and reliability capabilities of the scheme, regardless of the application bitrate, the number of clients, and the number of concurrent multicast streams. Furthermore, the channel utilization is greatly reduced with regard to the standard multicast schemes, which allows other applications to be supported without experiencing a performance degradation. We release the entire software implementation under a permissive APACHE 2.0 license for academic use.

In view of the exponential growth in the live multimedia content applications, multicast communications would imply a considerable traffic reduction compared to the unicast ones. However, given the absence of feedback information, packets are sent at the lowest rate, hence occupying the medium for long periods. Software Defined Networking (SDN) has changed the traditional network operations, therefore simplifying the network management and resource allocation. On this basis, in this demo we present an SDN-based algorithm for the dynamic multicast rate adaptation in Wi-Fi networks that outperforms the channel usage of the IEEE 802.11 standard, while maintaining the quality of the transmission. The performance of our solution has been preliminarily tested on a real-world testbed, therefore proving how it can be run on any WLAN infrastructure.

Live multimedia applications have experienced a dramatic growth due to the development of High Definition (HD) contents. Most of these streams are targeted at groups of people, who usually use Wi-Fi networks to access the platforms. In this context, a renewed interest in multicast applications has arisen, which usually suffer low data rates and reliability issues. Software Defined Networking (SDN) has revolutionized the traditional network architecture management, hence allowing for new ways to address the most challenging network constraints. In this paper, SDN@Play is presented as an SDN-based algorithm to dynamically adapt the multicast data rate. The implementation over a real-world testbed has proved that this solution is able to outperform the channel occupancy rate of the IEEE 802.11 standard, while keeping the the reliability of the transmission. We release the entire implementation including the controller and the data-path under a permissive license for academic use.

The increasing demand for multimedia content and for live broadcasting is bringing renewed interest in multicast applications. In many cases, users access such streams using Wi-Fi networks. However, multicast over Wi-Fi poses several challenges including low-data rates and coexistence issues with regard to other unicast streams. Software Defined Networking (SDN) has recently emerged as a novel approach to network control and management. In this paper we present SDN@Play, a novel SDN-based solution for multicast rate-adaptation in Wi-Fi networks. The solution builds upon a new abstraction, named Transmission Policy which allows the SDN controller to reconfigure or replace a certain rate control policy if its optimal operating conditions are not met. An experimental evaluation carried out over a real-world testbed shows that this approach can deliver an improvement of up to 80% in terms of channel utilization compared to legacy 802.11 multicast. We release the entire implementation including the controller and the data-path under a permissive license for academic use.

The high demand for wireless connectivity and the increase in the applications bitrate have lead to deploy denser and heterogeneous networks. However, an inefficient management of the network resources may arise poor performance and collision issues, therefore presenting a new set of challenges. In this demo, we will leverage on the Software Defined Networking (SDN) paradigm to show Wi-Balance, an algorithm able to achieve an effective balance of the traffic load in Wi-Fi networks with the aim of providing an optimum distribution of the network resources and improving the global performance.

Multimedia communications have experienced an unprecedented growth due mainly to the increase in the content quality and the emergence of smart devices. The demand for these contents is tending towards wireless technologies. However, these transmissions are quite sensitive to network delays. Therefore, ensuring an optimum QoS level becomes of great importance. The IEEE 802.11e amendment was released to address the lack of QoS capabilities in the original IEEE 802.11 standard. Accordingly, the Enhanced Distributed Channel Access (EDCA) function was introduced, allowing it to differentiate traffic streams through a group of Medium Access Control (MAC) parameters. Although EDCA recommends a default configuration for these parameters, it has been proved that it is not optimum in many scenarios. In this work a dynamic prediction scheme for these parameters is presented. This approach ensures an appropriate traffic differentiation while maintaining compatibility with the stations without QoS support. As the APs are the only devices that use this algorithm, no changes are required to current network cards. The results show improvements in both voice and video transmissions, as well as in the QoS level of the network that the proposal achieves with regard to EDCA.

The incessant development of High Quality (HQ) multimedia contents and the trend towards the use of wireless technologies have as a consequence the need for providing the users with an adequate level of Quality of Service (QoS) in IEEE 802.11 networks. The IEEE 802.11e amendment aims to overcome this situation by introducing the Enhanced Distributed Channel Access (EDCA) access method. This new method is characterised through a group of Medium Access Control (MAC) parameters, which are able to classify and prioritize the different types of traffic. In this regard, the most determining parameter is the Arbitration Inter-Frame Space Number (AIFSN). On this basis, we propose a new adaptation scheme that makes use of a M5 regression model with the aim of improving the voice and video performance offered by EDCA. Our proposal is able to determine dynamically the optimum AIFSN values with regard to the network conditions, maintaining the backward compatibility with the stations that use the original IEEE 802.11 standard. The prediction algorithm is only queried by the Access Point (AP), without introducing additional control traffic into the network, making it possible to use it in real-time. With respect to the standard EDCA values, the results show an enhancement in the voice+video normalized throughput and a significant reduction in the number of the retransmission attempts.

The original version of the IEEE 802.11 standard is not able to provide the required Quality of Service (QoS) for real-time applications. The IEEE 802.11e amendment was developed to overcome this situation, introducing the Enhanced Distributed Channel Access (EDCA) as a new channel access method. This method makes it possible to prioritize the different types of traffic through a group of Medium Access Control (MAC) parameters. The most important role of these MAC parameters is played by the Arbitration Inter-Frame Space Number (AIFSN). Although the AIFSN can be modified during a transmission, the commercial Access Points (APs) only use the combination defined in the standard. Therefore, we propose a new adaptation scheme for network traffic priorities involving the construction of a J48 decision tree classifier with the main goal of enhancing the voice and video communications. This classifier calculates a new set of AIFSN values by taking into account the current network conditions, maintaining the interoperability with the legacy Distributed Coordination Function (DCF) applications. The results show that our proposal improves upon the voice+video performance results obtained by the AIFSN standard values by up to 20%. Furthermore, this scheme is fully compatible with the commercial network cards available on the market.

Nowadays, networks and terminals with diverse characteristics of bandwidth and capabilities coexist. To ensure a good quality of experience, this diverse environment demands adaptability of the video stream. In general, video contents are compressed to save storage capacity and to reduce the bandwidth required for its transmission. Therefore, if these compressed video streams were compressed using scalable video coding schemes, they would be able to adapt to those heterogeneous networks and a wide range of terminals. Since the majority of the multimedia contents are compressed using H.264/AVC, they cannot benefit from that scalability. This paper proposes a technique to convert an H.264/AVC bitstream without scalability to a scalable bitstream with temporal scalability in Main Profile by accelerating the mode decision task of the SVC encoding stage using Machine Learning tools. The results show that when our technique is applied, the complexity is reduced by 87% while maintaining coding efficiency.

Scalable Video Coding (SVC) is a recent extension of the ISO/ITU Advanced Video Coding (H.264/AVC) standard, which allows adapting the bitstream easily by dropping some parts of it named as layers. This adaptation makes possible that the same bitstream meets the requirements for reliable delivery of video to diverse clients over heterogeneous networks using temporal, spatial or SNR scalability, combined or separately. Since the SVC design requires scalability to be provided at the encoder side, the existing contents cannot benefit of it. Efficient techniques for converting contents without scalability to a scalable format are desirable. In this paper, an approach for temporal scalability transcoding from H.264/AVC to SVC is presented and the impact of the GOP size is analyzed. Independently of the GOP size chosen, around a 60% of time saving is achieved while maintaining the coding efficiency. Moreover, this technique could be used to transform an H.264/AVC bitstream without temporal scalability to another H.264/AVC bitstream with hierarchical structures that provides temporal scalability.

Based on our previous efforts, we introduce in this letter a high-efficient MPEG-2 to H.264 transcoder for the baseline profile in the spatial domain. Machine learning tools are used to exploit the correlation between the macroblock (MB) decision of the H.264 video format and the distribution of the motion compensated residual in MPEG-2. Moreover, a dynamic motion estimation technique is also proposed to further speed-up the decision process. Finally, we go a step further on our previous research efforts by combining the two aforementioned speed-up approaches. Our simulation results over more than 40 sequences at common intermediate format and quarter common intermediate format resolutions show that our proposal outperforms the MB mode selection of the rate-distortion optimization option of the H.264 encoder process by reducing the computational requirements by up to 90%, while maintaining the same coding efficiency. Finally, we conduct a comparative study of our approach with the most relevant fast inter-prediction methods for MPEG-2 to H.264 transcoder recently reported in the literature.

Most of the reported distributed video coding (DVC) architectures have a serious limitation that hinders its practical application. The uses of a feedback channel between the encoder and the decoder require an interactive decoding procedure which is a limitation for applications such as offline processing. On the other hand, the decoder needs an efficient way to estimate the probability of error without assuming the availability of the original video at the decoder. In this paper we continue with our previous works into a more practical DVC architecture which solves both problems based on the use of machine learning. The proposed approach is based on extracting the relationships that exist between the residual frame and the number of requests over this feedback channel. We apply these concepts to pixel-domain Wyner-Ziv coding demonstrating significant savings in bitrates with a little loss of quality.

The H.264 standard achieves much higher coding efficiency than the MPEG-2 standard, due to its improved inter-and intra-prediction modes at the expense of higher computational complexity. Transcoding MPEG-2 video to H.264 is important to enable gradual migration to H.264. However, given the significant differences between the MPEG-2 and the H.264 coding algorithms, transcoding is a much more complex task and new approaches to transcoding are necessary. The main problems that need to be addressed in the design of an efficient heterogeneous MPEG-2/H.264 transcoder are: the inter-frame prediction, the transform coding and the intra-frame prediction. In this paper, we focus our attention on the inter-frame prediction, the most computationally intensive task involved in the transcoding process. This paper presents a novel macroblock (MB) mode decision algorithm for P-frame prediction based on machine learning techniques to be used as part of a very low complexity MPEG-2 to H.264 video transcoder. Since coding mode decisions take up the most resources in video transcoding, a fast MB mode estimation would lead to reduced complexity. The proposed approach is based on the hypothesis that MB coding mode decisions in H.264 video have a correlation with the distribution of the motion compensated residual in MPEG-2 video. We use machine learning tools to exploit the correlation and construct decision trees to classify the incoming MPEG-2 MBs into one of the several coding modes in H.264. The proposed approach reduces the H.264 MB mode computation process into a decision tree lookup with very low complexity. Experimental results show that the proposed approach reduces the MB mode selection complexity by as much as 95% while maintaining the coding efficiency. Finally, we conduct a comparative study with some of the most prominent fast inter-prediction methods for H.264 presented in the literature. Our results show that the proposed approach achieves the best results for video transcoding applications.

The H.264 standard, jointly developed by the ITU-T and the MPEG committees, is highly efficient offering perceptually equivalent quality video at about 1/3 to 1/2 of the bitrates offered by the MPEG-2 format. These significant bandwidth savings open the market to new products and services, including HDTV services at lower bitrates. It is expected that the H.264/AVC takes over the digital video market, replacing the use of MPEG-2 in most digital video applications. The complete migration to the new video-coding algorithm takes several years given the wide scale use of MPEG-2 in the market place today. This creates an important need for transcoding technologies for converting the large volume of existent video material from the MPEG-2 into the H.264 format and vice versa. However, given the significant differences between the MPEG-2 and the H.264 encoding algorithms, the transcoding process of such systems is much more complex to other heterogeneous video transcoding processes. In this paper, we introduce and evaluate a novel fast intra-frame prediction algorithm to be used as part of a high-efficient MPEG-2 to H.264 transcoder. Our results show that the proposed algorithm is able to maintain a good picture quality while considerably reducing the number of operations to be performed.

MPEG-2 is the most widely digital video-encoding standard in use nowadays. It is being widely used in the development and deployment of digital TV services, DVD and video-on-demand services. However, recent developments have given birth to the H.264/AVC, offering better bandwidth to video quality ratios than MPEG2. It is expected that the H.264/AVC will take over the digital video market, replacing the use of MPEG-2 in most digital video applications. The complete migration to the new video-coding algorithm will take several years given the wide scale use of MPEG-2 in the market place today. This creates an important need for transcoding technologies for converting the large volume of existent video material from the MPEG-2 into the H.264 format and vice versa. However, given the significant differences between the MPEG-2 and the H.264 encoding algorithms, the transcoding process of such systems is much more complex to other heterogeneous video transcoding processes. In this paper, we introduce and evaluate a novel intra-frame prediction algorithm to be used as part of a high-efficient MPEG-2 to H.264 transcoder. Our evaluation results show that the proposed algorithm considerable reduces the complexity involved in the intra-frame prediction: a key operation in the transcoding process

HIPERLAN/2 is a recent standard from ETSI for highspeed wireless LANs. This work explores this standard when providing QoS support to different traffic types. We describe a new bandwidth allocation protocol, which distinguishes among five types of connections, each one of them having its own QoS requirements. We carry out a performance evaluation via simulations that reflects the behavior of the novel MAC protocol. The bandwidth allocation protocol is evaluated in terms of its flexibility and efficiency in supporting delay sensitive traffic. We look particularly to MPEG-4 video communications, an application which is expected to be widely deployed in broadband wireless LANs.

Personal communication systems (PCS) aim to provide ubiquitous service to its subscribers. In order to fulfil the ubiquity requirement for service, PCS rely on two main features: use of wireless communications technology and mobility management. The former sets the basis to untie subscribers from a wire allowing them to freely move around. The latter allows subscribers to gain access to the network services at all times regardless of their location. A handover is the process used to allow a call in progress to continue as the associated subscriber moves around. A large number of handover mechanisms have been reported in the literature. In this article, we carry out a comprehensive performance study of the handover mechanisms as proposed in the literature. A detailed simulation model has been developed allowing us to set up parameters such as the speed and motion path of the mobile terminal. The performance metrics of interest are: the length of the disruption time caused by the handover mechanism and the number of lost packets caused by the handover mechanism. Under the scenario under consideration, the handovers using multicasting mechanism and full reestablishment mechanism exhibit the best results.

A large number of studies have been conducted to evaluate the benefits of using layered video coding schemes as a means to improve the robustness of video communications systems. In this article, we study a frame-aware layering scheme for the transport of DCT-based video over packet switched networks. Under this scheme, the relevance of the different elements of the video sequence is taken into account in order to compose the encoded video sequence. We conduct a detailed study over a large set of video streams encoded using the MPEG-2 video standard. Our results show that it is possible to reduce the video traffic while maintaining an acceptable video quality.

Two major issues in providing true end-to-end wireless/mobile video capabilities are: interoperability among network platforms and robustness of video compression algorithms in error-prone environments. In this paper, we mainly focus on the second issue and show how error resilience techniques can be used to improve the video quality. We argue that the error resilient tools provided within the MPEG-4 standard are not sufficient to provide acceptable quality in wireless/mobile networks, but that this quality can be significantly improved by the inclusion of hierarchical MPEG-4 video coding techniques. We present a novel hierarchical MPEG-4 video scheme particularly designed for video communications over QoS-capable wireless/mobile network.

MPEG-2 video communications over ATM networks is one of the most active research areas in the field of computer communications. In the transmission process of a variable bit rate video signal over an ATM network, cells are inevitably exposed to delays, errors, and losses due to the statistical multiplexing used in these networks. These phenomena affect the quality of the video signal, and without adequate measures to control the propagation of the impairments, the quality of the service may fall below acceptable levels. In this paper, we study the impact of cell losses on the quality of an MPEG-2 video sequence encoded in a variable bit rate mode. We introduce a set of control mechanisms at different levels of the protocol architecture to be used in MPEG-2-based video communications systems using ATM networks as their underlying transmission mechanism. Our results (using different video sequences) show the effectiveness to improve the video quality by using a structured set of control mechanisms to overcome for the loss of cells carrying VBR MPEG-2 video streams. We argue that in order to be able to create video systems able to cope with cell losses encountered in computer communications systems, a structured set of error-resilient protocol mechanisms is needed.

We introduce a set of control mechanisms at different levels of the protocol architecture to be used in MPEG-2-based video communications systems using ATM networks as their underlying transmission mechanism. Our results show the effectiveness of using a structured set of control mechanisms to overcome for the loss of cells carrying VBR video streams.

MPEG-2 video communications over ATM networks is one of the most active research areas in the field of computer communications. In this paper, we introduce a set of control mechanisms at different levels of the protocol architecture to be used in MPEG-2-based video communications systems using ATM networks as their underlying transmission mechanism. We argue that in order to be able to create video systems able to cope with some of the errors encountered in computer communications systems, a structured set of error-resilient protocol mechanisms is needed. Our results show the effectiveness of using a structured set of control mechanisms to overcome for the loss of cells carrying VBR MPEG-2 video streams.

We propose the implementation of a MPEG encoder developed by the University of California at Berkeley on a multicomputer system. Since this application is in real time, we present a mapping of the video sequence between the EPs of the architecture, where the communication between EPs is minimized. We also propose the necessary load/store process with a simple mechanism input/output, where the global distribution process latency is compensated. Idonety of the topology of the system is analyzed, together with the most adequate commutation technique for the interconnection network. Finally the incidence of the frame format on the system communication performance is analyzed.

We present a simulation tool of parallel architectures for image digital treatment applications, which is characterized by the simulation of different architectures based on RISC DLX processors and an interconnection network based on wormhole routing. Each node is provided with a computational DLX processor and another of similar features, devoted to control the communication network.