The explosion in the variety and volume of video services makes bandwidth and latency performance of networks more critical to the user experience. The media industry's response, HTTP-based Adaptive Streaming technology, offers media players the possibility to dynamically select the most appropriate bitrate according to the connectivity performance. Moving forward, the telecom industry's move is 5G. 5G aims efficiency by dynamic network optimization to make maximum use of the resources to get as high capacity and Quality of Service (QoS) as possible. These networks will be based on software defined networking (SDN) and network function virtualization (NFV) techniques, enabling self-management functions. Here, machine learning is a key technology to reach this 5G vision. On top of machine learning, SDN and NFV, this paper provides a network resource allocator system as the main contribution which enables autonomous network management aware of quality of experience (QoE). This system predicts demand to foresee the amount of network resources to be allocated and the topology setup required to cope with the traffic demand. Furthermore, the system dynamically provisions the network topology in a proactive way, while keeping the network operation within QoS ranges. To this end, the system processes signals from multiple network nodes and end-to-end QoS and QoE metrics. This paper evaluates the system for live and on-demand dynamic adaptive streaming over HTTP and high efficiency video coding services. From the experiment results, it is concluded that the system is able to scale the network topology and to address the level of resource efficiency, required by media streaming services.

The massive consumption of media contents needs of network accelerators, which boost the media delivery and optimize the traffic volume crossing the network from servers to media players. Content Delivery Network (CDN) is the common network function to distribute in a cloud-manner the contents, enhancing media availability and distribution performance. However, high concurrency rates of media sessions can produce CDN performance degradations and outages that impact negatively the Quality of Experience (QoE). 5G Multi-access Edge Computing (MEC) architecture envisions a QoE-aware system at the network edge which performs analytics to enhance or boost media services. This paper provides a novel MEC proxy to expand and enforce caching infrastructures for efficient and reliable content distribution. First, the proxy caches contents on the network edge to reduce the Capital Expenditure (CAPEX) of the CDN for the OTT service provider. To this end, the proxy is able to identify recurrent requests. Second, the proxy shields from identified or predicted CDN malfunction. Here, the proxy switches the download sessions to an alternative CDN in order to ensure QoE rates, enabling a CDN dynamic selection based on live connectivity statistics. The proposed solution is evaluated by delivering Dynamic Adaptive Streaming over HTTP (MPEG-DASH) streams in a dense client cell while applying different caching strategies.

łooseness1Owing to increasing consumption of video streams and demand for higher quality content and more advanced displays, future telecommunication networks are expected to outperform current networks in terms of key performance indicators (KPIs). Currently, content delivery networks (CDNs) are used to enhance media availability and delivery performance across the Internet in a cost-effective manner. The proliferation of CDN vendors and business models allows the content provider (CP) to use multiple CDN providers simultaneously. However, extreme concurrency dynamics can affect CDN capacity, causing performance degradation and outages, while overestimated demand affects costs. 5G standardization communities envision advanced network functions executing video analytics to enhance or boost media services. Network accelerators are required to enforce CDN resilience and efficient utilization of CDN assets. In this regard, this study investigates a cost-effective service to dynamically select the CDN for each session and video segment at the Media Server, without any modification to the video streaming pipeline being required. This service performs time series forecasts by employing a Long Short-Term Memory (LSTM) network to process real time measurements coming from connected video players. This service also ensures reliable and cost-effective content delivery through proactive selection of the CDN that fits with performance and business constraints. To this end, the proposed service predicts the number of players that can be served by each CDN at each time; then, it switches the required players between CDNs to keep the (Quality of Service) QoS rates or to reduce the CP's operational expenditure (OPEX). The proposed solution is evaluated by a real server, CDNs, and players and delivering dynamic adaptive streaming over HTTP (MPEG-DASH), where clients are notified to switch to another CDN through a standard MPEG-DASH media presentation description (MPD) update mechanism.

Network slicing is a promising technique used in the smart delivery of traffic and can satisfy the requirements of specific applications or systems based on the features of the 5G network. To this end, an appropriate slice needs to be selected for each data flow to efficiently transmit data for different applications and heterogeneous requirements. To apply the slicing paradigm at the radio segment of a cellular network, this paper presents two approaches for dynamically classifying the traffic types of individual flows and transmitting them through a specific slice with an associated 5G quality-of-service identifier (5QI). Finally, using a 5G standalone (SA) experimental network solution, we apply the radio resource sharing configuration to prioritize traffic that is dispatched through the most suitable slice. The results demonstrate that the use of network slicing allows for higher efficiency and reliability for the most critical data in terms of packet loss or jitter.

Beyond the advanced radio capabilities, 5G means a digital transformation, catalyzed by cloud technologies, making the networks agile and broader. However, high and quick dynamics in dense client cells consuming live broadcast contents can cause Quality of Experience (QoE) degradations. Here, inaccurate bandwidth assessment of media players drives to buffering times along with quality fluctuations. Moreover, massive recurrent requests can negatively impact on Content Delivery Network (CDN) performance. Complemented by capillarity and zero-latency features of multi-access edge computing (MEC) systems, 5G infrastructures will expand media services to take QoE to a new level. This paper investigates QoE gains of an MEC enabled infrastructure. The proposed MEC system applies three video delivery mechanisms. First, it enforces the QoE in a congested cell. Second, it shields from CDN degradation for a reliable content distribution. Third, it enhances network core and backhaul efficiency saving CDN traffic. Furthermore, our solution is deployed and tested on a LTE infrastructure. Results for live streams show that the MEC system makes the media players tend to a common and high quality bitrate, and it is able to quickly, transparently and coordinately switch to healthy CDN infrastructures and reduce CDN traffic.

Media services must ensure an enhanced user’s perceived quality during content playback to attract and retain audiences, especially while the streams are distributed remotely via networks. Thus, media streaming services rely heavily on good and predictable network performance when delivered to a large number of people. Furthermore, as the quality of media content gets high, the network performance demands are also increasing, and meeting them is challenging. Network functions devoted to improving media streaming services become essential to cope with the high dynamics of network performance and user mobility. Furthermore, new networking paradigms and architectures under the 5G networks umbrella are bringing new possibilities to deploy smart network functions, which monitor the media streaming services through live and objective metrics and boost them in real-time. This survey overviews the state-of-the-art technologies and solutions proposed to apply new network functions for enhancing media streaming.