Content-aware QoE Optimization in MEC-assisted Mobile Video Streaming

The traditional client-based HTTP adaptation strategies do not explicitly coordinate between the clients, servers, and cellular networks. A lack of coordination leads to suboptimal user experience. In addition to optimizing Quality of Experience (QoE), other challenges in adapting HTTP adaptive streaming (HAS) to the cellular environment are overcoming unfair allocation of the video rate and inefficient utilization of the bandwidth under the high-dynamics cellular links. Furthermore, the majority of the adaptive strategies ignore important video content characteristics and HAS client information, such as segment duration, buffer size, and video duration, in the video quality selection process. In this paper, we present a content-aware hybrid multi-access edge computing (MEC)-assisted quality adaptation algorithm by taking advantage of the capabilities of edge cloud computing. The proposed algorithm exploits video content characteristics, HAS client settings, and application-layer information to jointly adapt the bitrates of multiple clients. We design separate strategies to optimize the performance of short and long duration videos. We then demonstrate the efficiency of our algorithm against client-based solutions as well as MEC-assisted algorithms. The proposed algorithm guarantees high QoE, equitably selects video rates for clients, and efficiently utilizes the bandwidth for both short and long duration videos. The results from our extensive experiments reveal that the proposed long video adaptation algorithm outperforms state-of-the-art algorithms, with improvements in average video rate, QoE, fairness, and bandwidth utilization of 0.4%-12.3%, 8%-65%, 3.3%-5.7%, and 60%-130%, respectively. Furthermore, when high bandwidth is available to competing clients, the proposed short video adaptation algorithm improves QoE by 11.1% compared to the long video adaptation algorithm

Towards SVC-based adaptive streaming in information centric networks

HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for video streaming services. In HAS, each video is segmented and stored in different qualities. The client can dynamically select the most appropriate quality level to download, allowing it to adapt to varying network conditions. As the Internet was not designed to deliver such applications, optimal support for multimedia delivery is still missing. Information Centric Networking (ICN) is a recently proposed disruptive architecture that could solve this issue, where the focus is given to the content rather than to end-to-end connectivity. Due to the bandwidth unpredictability typical of ICN, standard AVC-based HAS performs quality selection sub-optimally, thus leading to a poor Quality of Experience (QoE). In this article, we propose to overcome this inefficiency by using Scalable Video Coding (SVC) instead. We individuate the main advantages of SVC-based HAS over ICN and outline, both theoretically and via simulation, the research challenges to be addressed to optimize the delivered QoE

Analysis and implementation of segment aware rate adaptation algorithms used in dynamic adaptive streaming

U današnjem svijetu velik dio internetskog prometa odlazi na prijenos video signala. Da bi se krajnjem korisniku mogla pružiti što bolja usluga, tijekom godina su razvijena razna rješenja. Jedno od popularnijih rješenja pod nazivom MPEG-DASH korišteno je u okviru ovog rada. MPEG-DASH se temelji na HTTP protokolu aplikacijskog sloja TCP/IP mrežnog modela i omogućava adaptivan prijenos video signala promjenom kvalitete video sadržaja ovisno o parametrima mreže. Kod MPEGDASH standarda video sadržaj je podijeljen na segmente jednakog trajanja koji su kodirani u više razina kvalitete i postavljeni na poslužitelju. Za odabir segmenata odgovarajuće razine kvalitete koriste se adaptacijski algoritmi smješteni na klijentskoj strani. Iako svi segmenti imaju jednako vremensko trajanje njihova veličina se razlikuje ovisno o sadržaju kojeg prenose. Radi bolje procjene vremena potrebnog za preuzimanje segmenta predlaže se korištenje podatka o veličini pojedinačnog segmenta pri odabiru. U ovom radu analizirana su i implementirana tri odabrana adaptacijska algoritma koji u obzir uzimaju veličinu segmenta. Nakon implementacije izvršena su mjerenja nad različitim video sekvencama, a rezultati su uspoređeni s osnovnim adaptacijskim algoritmom.Nowadays video streaming services make up majority of global Internet traffic. In order to provide best possible quality of service for users, various solutions were developed. One of the most widespread standards, named MPEG-DASH, is used in this paper. MPEG-DASH is based on application layer HTTP protocol of TCP/IP network model and enables adaptive video streaming considering current network parameters. Video content is divided into smaller segments of same duration encoded in different bit rates and stored on server. During media download client side software chooses segments of appropriate bit rate by using adaptation algorithms. Although each segment has the same duration, their sizes vary depending on content. To provide better estimation of time required to download next segment, it was suggested to utilize information about size of each segment. In this paper three segment aware adaptation algorithms were analyzed and implemented. Afterwards their performance was tested and results were compared with results achieved by basic adaptation algorithm

Evaluation of HTTP/DASH Adaptation Algorithms on Vehicular Networks

Video streaming currently accounts for the majority of Internet traffic. One factor that enables video streaming is HTTP Adaptive Streaming (HAS), that allows the users to stream video using a bit rate that closely matches the available bandwidth from the server to the client. MPEG Dynamic Adaptive Streaming over HTTP (DASH) is a widely used standard, that allows the clients to select the resolution to download based on their own estimations. The algorithm for determining the next segment in a DASH stream is not partof the standard, but it is an important factor in the resulting playback quality. Nowadays vehicles are increasingly equipped with mobile communication devices, and in-vehicle multimedia entertainment systems. In this paper, we evaluate the performance of various DASH adaptation algorithms over a vehicular network. We present detailed simulation results highlighting the advantages and disadvantages of various adaptation algorithms in delivering video content to vehicular users, and we show how the different adaptation algorithms perform in terms of throughput, playback interruption time, and number of interruptions