Dirk Staehle

Design considerations for a 5G network architecture

This article presents an architecture vision to address the challenges placed on 5G mobile networks. A two-layer architecture is proposed, consisting of a radio network and a network cloud, integrating various enablers such as small cells, massive MIMO, control/user plane split, NFV, and SDN. Three main concepts are integrated: ultra-dense small cell deployments on licensed and unlicensed spectrum, under control/user plane split architecture, to address capacity and data rate challenges; NFV and SDN to provide flexible network deployment and operation; and intelligent use of network data to facilitate optimal use of network resources for QoE provisioning and planning. An initial proof of concept evaluation is presented to demonstrate the potential of the proposal. Finally, other issues that must be addressed to realize a complete 5G architecture vision are discussed.

Quality-of-experience driven adaptive HTTP media delivery

This paper presents a Quality of Experience (QoE) driven approach for multi-user resource optimization in Dynamic Adaptive Streaming over HTTP (DASH) over next generation wireless networks. Our objective is to enhance the user experience in adaptive HTTP streaming by jointly considering the characteristics of the media content and the available wireless resources in the operator network. Specifically, we propose a proactive QoE-based approach for rewriting the client HTTP requests at a proxy in the mobile network. The advantage of the proposed approach is its applicability for over-the-top (OTT) streaming as it requires no adaptation of the media content. We compare our proposed scheme to both reactive QoE-optimized and to standard-DASH HTTP streaming. Our contributions are: 1) We first show that standard OTT DASH leads to unsatisfactory performance since the content agnostic resource allocation by the LTE scheduler is far from optimal, and we can achieve a clear QoE improvement when considering the content characteristics. 2) We additionally show that proactively rewriting the client requests gives control of the video content adaptation to the network operator which has better information than the client on the load and radio conditions in the cell. This results in additional gains in user perceived video quality. 3) A standard unmodified DASH client remains unaware of the proposed rewriting of the HTTP requests and can decode and play the redirected media segments.

QoE-Based Traffic and Resource Management for Adaptive HTTP Video Delivery in LTE

There is a growing interest in over-the-top (OTT) dynamic adaptive streaming over Hypertext Transfer Protocol (HTTP) (DASH) services. In mobile DASH, a client controls the streaming rate and the base station in the mobile network decides on the resource allocation. Different from the majority of previous works that focus on client-based rate adaptation mechanisms, this paper investigates the mobile network potential for enhancing the user quality-of-experience (QoE) in multiuser OTT DASH. Specifically, we first present proactive and reactive QoE optimization approaches for adapting the adaptive HTTP video delivery in an long-term evolution network. We then show, using subjective experiments, that by taking a proactive role in determining the transmission and streaming rates, the network operator can provide a better video quality and a fairer QoE across the streaming users. Furthermore, we consider the playout buffer time of the clients and propose a novel playout buffer-dependent approach that determines for each client the streaming rate for future video segments according to its buffer time and the achievable QoE under current radio conditions. In addition, we show that by jointly solving for the streaming and transmission rates, the wireless network resources are more efficiently allocated among the users and substantial gains in the user perceived video quality can be achieved.

QoE-aware priority marking and traffic management for H.264/SVC-based mobile video delivery

Mobile network operators experience a rapid increase of traffic in their networks that is mainly driven by the growing popularity of mobile video applications. They partly compensate this traffic increase by improved transmission technology and network densification. Supporting videos at high quality during peak traffic, however, does not scale economically. Instead, means are required for the operator to adapt the bitrate of the videos to the current network situation. Scalable Video Coding (SVC) provides a convenient way to adapt the bitrates of video streams in the network by dropping bit stream layers. From resource utilization point of view, the limitation of SVC is that the layer structure does not allow an operator to compare the importance of two layers of different videos with respect to their contribution to the user-perceived video quality. In existing solutions, the limitation is resolved by implementing sophisticated optimization algorithms at network bottlenecks for optimal rate adaptation among multiple videos. We take a different approach to eliminate the overhead of signaling and implementations introduced by the optimizations at bottlenecks while targeting at the same optimization goal. We propose a QoE-aware priority marking algorithm for SVC-based video streaming. In our approach, SVC layers are mapped to a finite number of priorities which are marked in video packets to be transmitted with video streams. This mapping takes into account the data rate and the quality contribution of a layer among layers of a group of videos. At network bottlenecks, rate adaptation simply follows the pre-determined priorities without further optimization needed. Our results show that the QoE-aware priority marking algorithm indeed overcomes the limitations of existing SVC packet marking and has the potential to achieve a QoE-optimal resource utilization in wireless access networks.

Low-Complexity No-Reference PSNR Estimation for H.264/AVC Encoded Video

We present a no-reference (NR) PSNR estimation method which is based on only two bitstream features (average bitrate and mean quantization parameter of the I-frames). The low computational complexity of the proposed method makes it suitable for in-network real-time applications. The NR metric achieves a Pearson correlation of 0.99 for individual videos and a RMSE of approximately 1 dB PSNR on average. We additionally investigate the effect of various encoding configurations on the PSNR and show the robustness of our method towards these. Finally, we incorporate the proposed metric into an example application and demonstrate that only a minor performance loss is observed compared to the reference scheme which assumes the availability of true PSNR information.

QoE-Based SVC Layer Dropping in LTE Networks Using Content-Aware Layer Priorities

The increasing popularity of mobile video streaming applications has led to a high volume of video traffic in mobile networks. As the base station, for instance, the eNB in LTE networks, has limited physical resources, it can be overloaded by this traffic. This problem can be addressed by using Scalable Video Coding (SVC), which allows the eNB to drop layers of the video streams to dynamically adapt the bitrate. The impact of bitrate adaptation on the Quality of Experience (QoE) for the users depends on the content characteristics of videos. As the current mobile network architectures do not support the eNB in obtaining video content information, QoE optimization schemes with explicit signaling of content information have been proposed. These schemes, however, require the eNB or a specific optimization module to process the video content on the fly in order to extract the required information. This increases the computation and signaling overhead significantly, raising the OPEX for mobile operators. To address this issue, in this article, a content-aware (CA) priority marking and layer dropping scheme is proposed. The CA priority indicates a transmission order for the layers of all transmitted videos across all users, resulting from a comparison of their utility versus rate characteristics. The CA priority values can be determined at the P-GW on the fly, allowing mobile operators to control the priority marking process. Alternatively, they can be determined offline at the video servers, avoiding real-time computation in the core network. The eNB can perform content-aware SVC layer dropping using only the priority values. No additional content processing is required. The proposed scheme is lightweight both in terms of architecture and computation. The improvement in QoE is substantial and very close to the performance obtained with the computation and signaling-intensive QoE optimization schemes.

QoS-aware composite scheduling using fuzzy proactive and reactive controllers

We consider in this paper downlink scheduling for different traffic classes at the MAC layer of wireless systems based on orthogonal frequency division multiple access (OFDMA), such as the recent 3rd Generation Partnership Project (3GPP) long-term evolution (LTE)/LTE-A wireless standard. Our goal is to provide via the scheduling decisions quality of service (QoS), but also to guarantee fairness among the different users and traffic classes (including delay-sensitive and best-effort traffic). QoS-aware scheduling strategies, such as modified largest weighted delay first (M-LWDF), exponential (EXP), exponential proportional fair (EXP-PF), and the log-based scheduling rules, prioritize delay-sensitive traffic by considering rules based on the head-of-line (HoL) packet delay and the tolerated packet loss rate, whereas they serve best-effort traffic by considering the classical proportional fair (PF) rule. These scheduling rules do not prevent resource starvation for best-effort traffic. On the other side, if both traffic types are scheduled according to the PF rule, then delay-sensitive flows suffer from delay bound violations. In order to fairly distribute the resources among different service classes according to their QoS requirements and channel conditions, we employ the concept of fuzzy logic in our scheduling framework. By employing the fuzzy logic concept, we serve all the traffic classes with one priority rule. Simulation results show better intra-class and inter-class fairness than state-of-the-art scheduling rules. The proposed scheduling framework enables to appropriately balance delay requirements of traffic, system throughput, and fairness.

Opportunistic QoS-Aware Fair Downlink Scheduling for Delay Sensitive Applications Using Fuzzy Reactive and Proactive Controllers

We consider in this paper downlink scheduling at the MAC layer for delay sensitive traffic in wireless systems based on Orthogonal Frequency Division Multiple Access (OFDMA), such as the recent LTE/LTE-A wireless standard. In the literature, time diversity is exploited in scheduling by using the Head of Line (HoL) packet delay and time-averaged bit throughput. However, under diverse channel conditions and variable rate characteristics of video traffic, the average throughput along with the HoL packet delay does not provide a fair system. We advocate here that, instead of utilizing the average throughput, the utilization of time-averaged channel quality along with the HoL packet delay and Packet Loss Rate (PLR) can reduce the Quality of Service (QoS) violations of low channel quality users and thus provide a fair system.

Opportunistic Proportional Fair Downlink Scheduling for Scalable Video Transmission over LTE Systems

We consider in this paper downlink scheduling at the MAC layer for delay sensitive traffic in wireless systems based on Orthogonal Frequency Division Multiple Access (OFDMA). We propose a downlink scheduling strategy for scalable video transmission to multiple users over OFDMA systems, such as the recent LTE/LTE-A wireless standard. We use a payoff metric based on video frame importance in conjunction with our proposed scheduler. The priority function on each Physical Resource Block (PRB), considering video frame importance and bit throughput, efficiently exploits multi-user diversity as compared to state-of-the-art strategies. Simulation results confirm the efficient utilization of time and frequency diversity by showing an improvement in the objective video quality of each user as compared to state-of-the-art strategies.