HTTP-based Adaptive Streaming Research Articles

PEPesc: A TCP Performance Enhancing Proxy for Non-Terrestrial Networks

Non-terrestrial networks (NTNs) using flying objects such as satellites play key roles in the next-generation wireless system (6 G). The NTN links with long propagation delay and random packet losses pose a great challenge to the performance of Transport Control Protocol (TCP), which many Internet applications rely on. Performance enhancing proxy (PEP) is an easy-to-deploy approach for improving TCP's performance. In this paper, we design and implement a novel PEP called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PEPesc</i> which has two distinctive features. First, it features <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">retransmission-free</i> loss recovery, using an adaptive packet-level forward erasure correction method called streaming coding (SC). Second, as packet losses are recovered by SC, the congestion control problem is simplified to rate control and local acknowledgement between entities based on bandwidth estimation. Based on a queueing theoretic analysis of the design, we carefully devise a protocol and implement PEPesc as an open-source application. Extensive evaluations show that PEPesc can achieve much higher <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">and</i> smoother goodput than the canonical TCP variants and than other existing open-source PEPs in applications including <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iperf</i> and HTTP-based adaptive streaming, and achieves similar performance in web browsing. Finally, we also present a deployment case over a real-world geostationary satellite link.

Timely and Accurate Bitrate Switching in HTTP Adaptive Streaming With Date-Driven I-Frame Prediction

In today's Internet, bandwidth dynamics are inevitable, and hence, the bitrate for live streaming applications should also be dynamically adjusted. However, in existing HTTP-based adaptive streaming (HAS), bitrate switching can only be performed at segment boundaries, making decisions unresponsive and often inaccurate. In this paper, we start from a close investigation on the impact of the segment length in HAS and accordingly present VHAS, an extension towards intelligent variable-length segmentation, which makes client-side decisions based on the massive amount of real-time information from the network and viewers. VHAS implements a smart trigger mechanism that balances accuracy and overhead for variable-length segmentation. We further develop an adaptive bitrate switching algorithm with data-driven I-frame prediction, which is tailored to individual viewers to minimize bitrate mismatches. We evaluate VHAS via extensive trace-driven simulations, and our results demonstrate that compared with state-of-the-art solutions, VHAS achieves 15% - 49% gains in QoE, with a noticeable bandwidth reduction of 37% - 57%.

A bio-inspired managed video delivery service using HTTP-based adaptive streaming

A bio-inspired managed video delivery service using HTTP-based adaptive streaming

AVQBits—Adaptive Video Quality Model Based on Bitstream Information for Various Video Applications

The paper presents <i>AV QBits</i>, a versatile, bitstream-based video quality model. It can be applied in several contexts such as video service monitoring, evaluation of video encoding quality, of gaming video QoE, and even of omnidirectional video quality. In the paper, it is shown that <i>AV QBits</i> predictions closely match video quality ratings obained in various subjective tests with human viewers, for videos up to 4K-UHD resolution (Ultra-High Definition, 3840 x 2180 pixels) and framerates up 120 fps. With the different variants of <i>AV QBits</i> presented in the paper, video quality can be monitored either at the client side, in the network or directly after encoding. The no-reference <i>AV QBits</i> model was developed for different video services and types of input data, reflecting the increasing popularity of Video-on-Demand services and widespread use of HTTP-based adaptive streaming. At its core, <i>AV QBits</i> encompasses the standardized ITU-T P.1204.3 model, with further model instances that can either have restricted or extended input information, depending on the application context. Four different instances of <i>AV QBits</i> are presented, that is, a Mode 3 model with full access to the bitstream, a Mode 0 variant using only metadata such as codec type, framerate, resoution and bitrate as input, a Mode 1 model using Mode 0 information and frame-type and -size information, and a Hybrid Mode 0 model that is based on Mode 0 metadata and the decoded video pixel information. The models are trained on the authors&#x2019; own AVT-PNATS-UHD-1 dataset described in the paper. All models show a highly competitive performance by using AVT-VQDB-UHD-1 as validation dataset, e.g., with the Mode 0 variant yielding a value of 0.890 Pearson Correlation, the Mode 1 model of 0.901, the hybrid no-reference mode 0 model of 0.928 and the model with full bitstream access of 0.942. In addition, all four <i>AV QBits</i> variants are evaluated when applying them out-of-the-box to different media formats such as 360&#x00B0; video, high framerate (HFR) content, or gaming videos. The analysis shows that the ITU-T P.1204.3 and Hybrid Mode 0 instances of <i>AV QBits</i> for the considered use-cases either perform on par with or better than even state-of-the-art full reference, pixel-based models. Furthermore, it is shown that the proposed Mode 0 and Mode 1 variants outperform commonly used no-reference models for the different application scopes. Also, a long-term integration model based on the standardized ITU-T P.1203.3 is presented to estimate ratings of overall audiovisual streaming Quality of Experience (QoE) for sessions of 30 s up to 5 min duration. In the paper, the <i>AV QBits</i> instances with their per-1-sec score output are evaluated as the video quality component of the proposed long-term integration model. All <i>AV QBits</i> variants as well as the long-term integration module are made publicly available for the community for further research.

Modular Framework and Instances of Pixel-Based Video Quality Models for UHD-1/4K

The popularity of video on-demand streaming services increased tremendously over the last years. Most services use http-based adaptive video streaming methods. Today’s movies and TV shows are typically recorded in UHD-1/4K and streamed using settings attuned to the end-device and current network conditions. Video quality prediction models can be used to perform an extensive analysis of video codec settings to ensure high quality. Hence, we present a framework for the development of pixel-based video quality models. We instantiate four different model variants ( hyfr , hyfu , fume and nofu ) for short-term video quality estimation targeting various use cases. Our models range from a no-reference video quality model to a full-reference model including hybrid model extensions that incorporate client accessible meta-data. All models share a similar architecture and the same core features, depending on their mode of operation. Besides traditional mean opinion score prediction, we tackle quality estimation as a classification and multi-output regression problem. Our performance evaluation is based on the publicly available AVT-VQDB-UHD-1 dataset. We further evaluate the introduced center-cropping approach to speed up calculations. Our analysis shows that our hybrid full-reference model ( hyfr ) performs best, e.g. 0.92 PCC for MOS prediction, followed by the hybrid no-reference model ( hyfu ), full-reference model ( fume ) and no-reference model ( nofu ). We further show that our models outperform popular state-of-the-art models. The introduced features and machine-learning pipeline are publicly available for use by the community for further research and extension.

Towards Influence of Chunk Size Variation on Video Streaming in Wireless Networks

In recent years, the growth in popularity of mobile video streaming services is unbroken. There are tremendous demands for video streaming over wireless networks. Currently, most video streaming is over HTTP. Up to now, HTTP-based adaptive video streaming is standardized as DASH, where a client-side video player can dynamically pick the bitrate level according to the perceived network conditions. Actually, not only the available bandwidth drastically varies due to wireless network properties, but also the chunk sizes in the same bitrate level significantly fluctuate, which also influences the bitrate adaptation. However, existing bitrate adaptation algorithms mostly focus on available bandwidth but do not involve chunk size variation, leading to performance losses. In this paper, we theoretically analyze the influence of chunk size variation on bitrate adaptation performance in wireless networks. Based on DASH system features, we build a general model describing playback buffer evolution. Applying stochastic theories, we respectively analyze the influence of the chunk size variation on rebuffering probability, average bitrate, and bitrate switching interval. Furthermore, based on theoretical insights, we provide several suggestions for algorithm designing and rate encoding, and also design a simple bitrate adaptation algorithm. Extensive simulations verify our insights, suggestions, and designed algorithm effectiveness.

HDA Video Transmission Scheme for DASH

In recent years, with the development of Internet communication technology, more and more people tend to get information through the network. And people prefer multimedia video streaming services. Traditional streaming media technology cannot meet people's needs because of its limitations. Recently, HTTP-based Dynamic Adaptive Streaming Over HTTP (DASH) has emerged as a new approach. In this paper, we consider two hybrid digital and analog video transmission schemes for DASH and propose three methods of enhancing the video quality of experience (QoE) of DASH users. First of all, considering the importance of data to video quality, we propose an energy-aware recombination method of reorganizing and packaging data and define the priority of the generated DASH layer in transmission. Second, we design an adaptive bitrate allocation algorithm to improve bandwidth utilization and video quality. Finally, we propose a retransmission mechanism to address the situation in which the digital stream at the receiving end cannot be decoded due to channel distance errors. Experiments show that our two schemes are superior to traditional DASH schemes.

Multi-Model Standard for Bitstream-, Pixel-Based and Hybrid Video Quality Assessment of UHD/4K: ITU-T P.1204

The paper presents a series of three new video quality model standards for the assessment of sequences of up to UHD/4K resolution. They were developed in a competition within the International Telecommunication Union (ITU-T), Study Group 12, in collaboration with the Video Quality Experts Group (VQEG), over a period of more than two years. A large video quality test set with a total of 26 individual databases was created, with 13 used for training and 13 for validation and selection of the winning models. For each database, video quality laboratory tests were run with at least 24 subjects each. The 5-point Absolute Category Rating (ACR) scale was used for rating, calculating Mean Opinion Scores (MOS) as ground-truth. To represent today's commonly applied HTTP-based adaptive streaming context, the test sequences comprise a variety of encoding settings, bitrates, resolutions and framerates for the three codecs H.264/AVC, H.265/HEVC and VP9, applied to a wide range of source sequences of around 8 s duration. Processing was carried out with an FFmpeg-based processing chain developed specifically for the competition, and via upload and encoding through exemplary online streaming services. The resulting data represents the largest, lab-test-based dataset used for video quality model development to date, with a total of around 5,000 test sequences. The paper addresses the three models ultimately standardized in the P.1204 Recommendation series, resulting in different model types and for different applications: (i) Rec. P.1204.3, no-reference bitstream-based, with access to encoded bitstream information; (ii) P.1204.4, pixel-based, using information from the reference and the processed video, and (iii) P.1204.5, no-reference hybrid, using both bitstream-and pixel-information without knowledge of the reference. The paper outlines the development process and provides holistic details about the statistical evaluation, test databases, model algorithms and validation results, as well as a performance comparison with state-of-the-art models.

Adaptive Chunklets and AQM for Higher-Performance Content Streaming

Commercial streaming services such as Netflix and YouTube use proprietary HTTP-based adaptive streaming (HAS) techniques to deliver content to consumers worldwide. MPEG recently developed Dynamic Adaptive Streaming over HTTP (DASH) as a unifying standard for HAS-based streaming. In DASH systems, streaming clients employ adaptive bitrate (ABR) algorithms to maximise user Quality of Experience (QoE) under variable network conditions. In a typical Internet-enabled home, video streams have to compete with diverse application flows for the last-mile Internet Service Provider (ISP) bottleneck capacity. Under such circumstances, ABR algorithms will only act upon the fraction of the network capacity that is available, leading to possible QoE degradation. We have previously explored chunklets as an approach orthogonal to ABR algorithms, which uses parallel connections for intra-video chunk retrieval. Chunklets effectively make more bandwidth available for ABR algorithms in the presence of cross-traffic, especially in environments where Active Queue Management (AQM) schemes such as Proportional Integral controller Enhanced (PIE) and FlowQueue-Controlled Delay (FQ-CoDel) are deployed. However, chunklets consume valuable server/middlebox resources which typically handle hundreds of thousands of requests/connections per second. In this article, we propose ‘adaptive chunklets’ -- a novel chunklet enhancement that dynamically tunes the number of concurrent connections. We demonstrate that the combination of adaptive chunklets and FQ-CoDel is the most effective strategy. Our experiments show that adaptive chunklets can reduce the number of connections by almost 30% and consume almost 8% less bandwidth than fixed chunklets while providing the same QoE.

Comparative Evaluation of User Perceived Quality Assessment of Design Strategies for HTTP-based Adaptive Streaming

HTTP-based Adaptive Streaming (HAS) is the dominant Internet video streaming application. One specific HAS approach, Dynamic Adaptive Streaming over HTTP (DASH), is of particular interest, as it is a widely deployed, standardized implementation. Prior academic research has focused on networking and protocol issues, and has contributed an accepted understanding of the performance and possible performance issues in large deployment scenarios. Our work extends the current understanding of HAS by focusing directly on the impacts of choice of the video quality adaptation algorithm on end-user perceived quality. In congested network scenarios, the details of the adaptation algorithm determine the amount of bandwidth consumed by the application as well as the quality of the rendered video stream. HAS will lead to user-perceived changes in video quality due to intentional changes in quality video segments, or unintentional perceived quality impairments caused by video decoder artifacts such as pixelation, stutters, or short or long stalls in the rendered video when the playback buffer becomes empty. The HAS adaptation algorithm attempts to find the optimal solution to mitigate the conflict between avoiding buffer stalls and maximizing video quality. In this article, we present results from a user study that was designed to provide insights into “best practice guidelines” for a HAS adaptation algorithm. Our findings suggest that a buffer-based strategy might provide a better experience under higher network impairment conditions. For the two network scenarios considered, the buffer-based strategy is effective in avoiding stalls but does so at the cost of reduced video quality. However, the buffer-based strategy does yield a lower number of quality switches as a result of infrequent bitrate adaptations. Participants in buffer-based strategy do notice the drop in video quality causing a decrease in perceived QoE, but the perceived levels of video quality, viewer frustration, and opinions of video clarity and distortion are significantly worse due to artifacts such as stalls in capacity-based strategy. The capacity-based strategy tries to provide the highest video quality possible but produces many more artifacts during playback. The results suggest that player video quality has more of an impact on perceived quality when stalls are infrequent. The study methodology also contributes a unique method for gathering continuous quantitative subjective measure of user perceived quality using a Wii remote.

Using Session Modeling to Estimate HTTP-Based Video QoE Metrics From Encrypted Network Traffic

Understanding the user-perceived quality of experience (QoE) of HTTP-based video has become critical for content providers, distributors, and network operators. For network operators, monitoring QoE is challenging due to lack of access to video streaming applications, user devices, or servers. Thus, network operators need to rely on the network traffic to infer key metrics that influence video QoE. Furthermore, with content providers increasingly encrypting the network traffic, the task of QoE inference from passive measurements has become even more challenging. In this paper, we present a methodology called eMIMIC that uses passive network measurements to estimate key video QoE metrics for encrypted HTTP-based adaptive streaming (HAS) sessions. eMIMIC uses packet headers from network traffic to model an HAS session and estimate video QoE metrics, such as average bitrate and re-buffering ratio. We evaluate our methodology using network traces from a variety of realistic conditions and ground truth collected using a lab testbed for video sessions from three popular services, two video on demand (VoD) and one Live. eMIMIC estimates re-buffering ratio within 1% point of ground truth for up to 75% sessions in VoD (80% in Live) and average bitrate with error under 100 Kb/s for up to 80% sessions in VoD (70% in Live). We also compare eMIMIC with recently proposed machine learning-based QoE estimation methodology. We show that eMIMIC can predict average bitrate with 2.8%–3.2% higher accuracy and re-buffering ratio with 9.8%–24.8% higher accuracy without requiring any training on ground truth QoE metrics. Finally, we show that eMIMIC can estimate real-time QoE metrics with at least 89.6% accuracy in identifying buffer occupancy state and at least 85.7% accuracy in identifying average bitrate class of recently downloaded chunks.

Adaptive video streaming with current codecs and formats: Extensions to parametric video quality model ITU-T P.1203

Adaptive streaming is fast becoming the most widely used method for video delivery to the end users over the internet. The ITU-T P.1203 standard is the first standardized quality of experience model for audiovisual HTTP-based adaptive streaming. This recommendation has been trained and validated for H.264 and resolutions up to and including full-HD. The paper provides an extension for the existing standardized short-term video quality model mode 0 for new codecs i.e., H.265, VP9 and AV1 and resolutions larger than full-HD (e.g. UHD-1). The extension is based on two subjective video quality tests. In the tests, in total 13 different source contents of 10 seconds each were used. These sources were encoded with resolutions ranging from 360p to 2160p and various quality levels using the H.265, VP9 and AV1 codecs. The subjective results from the two tests were then used to derive a mapping/correction function for P.1203.1 to handle new codecs and resolutions. It should be noted that the standardized model was not re-trained with the new subjective data, instead only a mapping/correction function was derived from the two subjective test results so as to extend the existing standard to the new codecs and resolutions.

Adaptive bandwidth binning for bandwidth management

In this paper, we present a novel bandwidth management scheme that we call adaptive bandwidth binning (ABB). ABB is presented in the context of a DOCSIS cable network, but it has obvious applicability to downstream service on any shared medium access network in which all downstream traffic is scheduled by a single headend device such as a CMTS or wireless base station or access point.ABB is capable of providing approximate weighted max-min fair sharing of downstream bandwidth via a low-overhead scheduler that requires only a small number of permanently allocated queues. A modern delay-based active queue management (AQM) technique is employed to control delays.The performance of ABB is evaluated via ns-2 simulations in which workloads include FTP, HTTP-based adaptive streaming (HAS), and web traffic targeted for different tiered service quality levels. Our results show that ABB is able to provide approximate weighted max-min fair bandwidth allocation among responsive high bandwidth flows while isolating them from low bandwidth and latency sensitive flows. The use of CoDel in each ABB queue is effective in managing latency as required. The use of flow weights in ABB supports service tiering in which subscribers pay more for higher service rates.

PREPARE-Playback RatE and Priority Adaptive BitRate Selection

HAS (HTTP-based Adaptive Streaming) based on ABR (Adaptive Bitrate Rate selection) has become a prevalent solution to media transmission. In order to improve the performance of HAS, many studies focus on how to reduce the unfairness, the instability and the inefficiency in a multi-clients scenario. However, the unfairness problem of the multi-clients with different priority levels and different playback rates, and the instability problem caused by the changes of the playback rate are still unresolved. To solve these problems, a novel ABR algorithm called PREPARE is proposed in this paper. In addition to normally considered throughput and client buffer information in HAS system model, the client priority and the playback rate are also incorporated to make the system model more accurate. With the feedback from servers, PREPARE algorithm is able to estimate the bandwidth much precisely and to reduce the unfairness among the multi-clients. The stability in bitrate selection is improved by introducing the buffer status in conjunction with the real-time playback rate. Moreover, the buffer underflow is prevented in download scheduling by advancing download according to the real-time playback rate. NS-3 simulator is used to evaluate the performance of PREPARE. The performance results show that PREPARE reduces the unfairness among multi-clients by more than 42% comparing to other three algorithms including ESTC, PANDA, FDASH. Meanwhile, PREPARE simultaneously keeps high stability and high efficiency.

ANFIS-based rate adaptation scheme for adaptive streaming over HTTP

Recently, HTTP-based video streaming traffic has continued to increase. Therefore, video service providers have been using HTTP-based adaptive streaming (HAS) technology to reduce the traffic load of the HTTP server. Accordingly, many adaptive bit rate (ABR) schemes have been proposed to provide a high quality of experience (QoE) to video service clients. In this paper, we propose a new ABR scheme using an adaptive network-based fuzzy inference system (ANFIS), which is one of the neuro-fuzzy structures. The proposed scheme learns optimal fuzzy parameters by using (1) the learning ability of ANFIS and (2) the video streaming data providing high QoE to clients. Then, the bit rate of the next segment is determined according to these trained parameters. In the simulation using NS-3, we show that the proposed scheme selects the appropriate bit rate under various wireless network conditions and provides better QoE to clients than the existing schemes.

Queueing Theoretic Approach to Playout Buffer Model for HTTP Adaptive Streaming

HTTP-based adaptive streaming (HAS) has recently been widely deployed on the Internet. In the HAS system, a video content is encoded at multiple bitrates and the encoded video content is segmented into small parts of fixed durations. The HAS client requests a video segment and stores it in the playout buffer. The rate adaptation algorithm employed in HAS clients dynamically determines the video bitrate depending on the time-varying bandwidth. Many studies have shown that an efficient rate adaptation algorithm is critical to ensuring quality-of-experience in HAS systems. However, existing algorithms have problems estimating the network bandwidth because bandwidth estimation is performed on the client-side application stack. Without the help of transport layer protocols, it is difficult to achieve accurate bandwidth estimation due to the inherent segment-based transmission of the HAS. In this paper, we propose an alternative approach that utilizes the playout buffer occupancy rather than using bandwidth estimates obtained from the application layer. We start with a queueing analysis of the playout buffer. Then, we present a buffer-aware rate adaptation algorithm that is solely based on the mean buffer occupancy. Our simulation results show that compared to conventional algorithms, the proposed algorithm achieves very smooth video quality while delivering a similar average video bitrate.

Cooperative caching for HTTP-based adaptive streaming contents in cache-enabled radio access networks

The rapid growth of the number of mobile broadband subscribers has led to an exponential increase of the Internet traffic, of which a major part is video traffic. In 4G/5G mobile networks, the eNBs should cache the popular contents to reduce the transit cost at the backhaul links. Recently, HTTP-based adaptive streaming is widely used to transfer the video contents to the end users in the Internet. With HTTP-based adaptive streaming, each video content is stored in several representations corresponding to different video performances. The representation with a better performance yields a higher user satisfaction, however, it consumes more storage and transit cost. In this paper, we propose a cooperative caching model in which the eNBs collaborate in caching and request routing the video contents. The proposed optimization problem is a large-scale integer linear program which is an NP-hard problem. Based on alternating direction method of multipliers technique, a distributed algorithm converging to the solution to the relaxation problem is then developed. It results to fractional caching and request routing which serve as a performance benchmark of the cooperative caching. Moreover, we propose a nearest-neighbor request routing policy and a lightweight-cooperative eviction algorithm for integral caching. Extensive simulations show that the proposed request routing and eviction algorithms achieve more than 78% of the performance benchmark.

Exploiting Video Quality Information With Lightweight Network Coordination for HTTP-Based Adaptive Video Streaming

This paper investigates how video-quality information can be exploited by HTTP-based adaptive streaming clients in their rate adaptation schemes. We also seek to answer the question: How much network coordination is required to achieve quality fairness across multiple competing clients? To that end, we envision a loosely coupled architecture where a lightweight centralized coordinator complements multiple federated quality-aware clients. Instead of actively programming network bandwidth allocation to each client, in our proposed architecture, the central coordinator simply collects quality and buffer level information from individual clients and publishes the aggregate quality statistics as reference. Such global reference statistics help to guide each federated client to self-tune its aggressiveness in its quality-aware adaptation scheme. Testbed-based evaluations show that such a lightweight approach is effective in reaping most of the performance gains previously achieved by a centralized joint optimization scheme. Compared to both state-of-the-art rate-based clients and independent quality-aware clients, our proposed federated quality-aware scheme can save up to 50% of total bandwidth while maintaining comparable aggregate video quality across all clients.

Video Quality Adaptation for Extended Playback Time on Mobile Devices With Limited Energy

In this letter, a HTTP-based adaptive rate control streaming (HARCS) scheme that selects the video quality level of each chunk for extending playback time is proposed. For this purpose, the expected energy consumption for downloading remaining chunks to be received is stochastically modeled by considering the time-varying wireless network conditions and video characteristics. Then, based on the derived energy model as well as the mobile device’s remaining battery energy, the proposed HARCS scheme adaptively selects the video quality level of each chunk. Measurement results show that HARCS can extend the video playback time up to 57.64% when compared with the conventional adaptation scheme that only considers the wireless network status.

Network Resource Allocation System for QoE-Aware Delivery of Media Services in 5G Networks

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.