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Internet video streaming applications have been demanding more bandwidth and higher video quality, especially with the advent of virtual reality and augmented reality appli-cations. While adaptive strea ming protocols like MPEG-DASH (dynamic adaptive streaming over HTTP) allows video quality to be flexibly adapted, e.g., degraded when mobile network condition deteriorates, this is not an option if the application itself requires guaranteed 4K quality at all time. On the other hand, conventional end-to-end transmission control protocol (TCP) has been struggling in supporting 4K video delivery across long-distance Internet paths containing both fixed and mobile network segments with heterogeneous characteristics. In this paper, we present a novel and practically feasible system architecture named MVP (mobile edge virtualization with adaptive prefetching), which enables content providers to embed their content intelligence as a virtual network function into the mobile network operator's infrastructure edge. Based on this architecture, we present a context-aware adaptive video prefetching scheme in order to achieve quality of experience (QoE)-assured 4K video on demand (VoD) delivery across the global Internet. Through experiments based on a real LTE-A network infrastructure, we demonstrate that our proposed scheme is able to achieve QoE-assured 4K VoD streaming, especially when the video source is located remotely in the public Internet, in which case none of the state-of-the-art solutions is able to support such an objective at global Internet scale.

In this paper, we present a Mobile Edge Computing (MEC) scheme for enabling network edge-assisted video adaptation based on MPEG-DASH (Dynamic Adaptive Streaming over HTTP). In contrast to the traditional over-the-top (OTT) adaptation performed by DASH clients, the MEC server at the mobile network edge can capture radio access network (RAN) conditions through its intrinsic Radio Network Information Service (RNIS) function, and use the knowledge to provide guidance to clients so that they can perform more intelligent video adaptation. In order to support such MEC-assisted DASH video adaptation, the MEC server needs to locally cache the most popular content segments at the qualities that can be supported by the current network throughput. Towards this end, we introduce a two-dimensional user Quality-of-Experience (QoE)-driven algorithm for making caching / replacement decisions based on both content context (e.g., segment popularity) and network context (e.g., RAN downlink throughput). We conducted experiments by deploying a prototype MEC server at a real LTE-A based network testbed. The results show that our QoE-driven algorithm is able to achieve significant improvement on user QoE over 2 benchmark schemes.

Jonathan Rodriguez. all data flowing between the targeted end user and the network. This attack exploits the unprotected user traffic in some part of the HeNB . For that reason, unprotected user data should never leave a secure domain inside ...

With ever increasing penetration of IP technologies and the tremendous growth in wireless data traffic, the wireless industry is evolving the mobile core networks towards IP technology. The 3rd Generation Partnership Project (3GPP) is specifying an IP Multimedia Core Network Sub-System (IMS) in UMTS Release 5/6, which is adjunct to the UMTS GPRS CN. This IP-based network will allow mobile operators to provide commonly used Internet applications to wireless user. The UMTS IMS uses the Internet Engineering Task Force (IETF) defined text based Session Initiation Protocol (SIP), to control a wide range of anticipated IP-based services. Text based protocols are easier to develop than bit-wise presentations and likely to be more expediently taken to market, as they allow vendors/operators to open the session/application space to a much broader development community. This is likely to offer new services such as wireless multimedia calls, chat, presence and push services. This paper presents results of a performance evaluation of the SIP based Presence and Instant Messaging service as being standardised by 3GPP and IETF based on a model implemented in a UMTS system simulator called the Framework for Radio Architecture Modeling (FRAM).

3GPP Rel.5 specifies an IP multimedia core network sub-system (IMS) adjunct to the UMTS packet switched (PS) GPRS CN bearer and Rel.6 will let UMTS provide full packet call control (CC) capabilities (e.g. security, emergency and QoS support). The packet call control capabilities employ the IETF text based call control protocol SIP (session initiation protocol). Text based protocols have the advantage that they are generally easier to develop than bit-wise presentations. However, due to their larger message sizes, delays associated with packet call control using SIP have concerned operators about the viability of such services over a UMTS air interface. This has led development of message compression techniques for more efficient use of SIP. This paper provides a brief overview of GSM, UMTS circuit switched call control, and SIP based call control as applied to UMTS and then compares and contrasts the relative performance of the call control protocols using results derived from a UMTS system simulator called the Framework for Radio Architecture Modelling (FRAM).

End-to-End (ETE) Quality of Service (QoS) is critical for next generation wireless multimedia communication systems. To meet the ETE QoS requirements, Universal Mobile Telecommunication System (UMTS) requires not only meeting the 3GPP QoS requirements [1-2] but also mapping external network QoS classes to UMTS QoS classes. There are four Quality of Services (QoS) classes in UMTS; they are Conversational, Streaming, Interactive and Background. There are eight QoS classes for LAN in IEEE 802.1 (one reserved). ATM has four QoS categories. They are Constant Bit Rate (CBR) - highest priority, short queue for strict Cell Delay Variation (CDV), Variable Bit Rate (VBR) - second highest priority, short queues for real time, longer queues for non-real time, Guaranteed Frame Rate (GFR)/ Unspecified Bit Rate (UBR) with Minimum Desired Cell Rate (MDCR) - intermediate priority, dependent on service provider UBR/ Available Bit Rate (ABR) - lowest priority, long queues, large delay variation. DiffServ (DS) has six-bit DS codepoint (DSCP) available to determine the datagram's priority relative to other datagrams and therefore, up to 64 QoS classes are available from the IPv4 and IPv6 DSCP. Different organisations have tried to solve the QoS issues from their own perspective. However, none of them has a full picture for end-to-end QoS classes and how to map them among all QoS classes. Therefore, a universal QoS needs to be created and a new set of QoS classes to enable end-to-end (ETE) QoS provisioning is required. In this paper, a new set of ETE QoS classes is proposed and a mappings algorithm for different QoS classes that are proposed by different organisations is given. With our proposal, ETE QoS mapping and control can be implemented.© (2002) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

In this paper, the design, construction, and testing of a fully-functional virtualised mobile core network is outlined. Lessons learned and recommendations for future improvements are provided. The presented work uses open-source software for virtual network function infrastructure control (OpenStack), network flow programming (OpenDaylight), and network orchestration (OpenBaton) to virtualise a commercial software evolved packet core solution deployed on common off-the-shelf hardware. The findings presented in this paper prove the concept of function virtualisation for mobile networks, and paves the way towards future mobile core network function flexibility as required for 5G networks. The paper provides researchers and network operators with first-hand experience to help build similar virtual mobile network infrastructures, and highlights the challenges to tackle and the issues to address to harness the power of virtualisation in 5G networks.