Video traffic on the Internet is constantly growing; networked multimedia applications consume a predominant share of the available Internet bandwidth. A major technical breakthrough and enabler in multimedia systems research and of industrial networked multimedia services certainly was the HTTP Adaptive Streaming (HAS) technique. This resulted in the standardization of MPEG Dynamic Adaptive Streaming over HTTP (MPEG-DASH) which, together with HTTP Live Streaming (HLS), is widely used for multimedia delivery in today’s networks. Existing challenges in multimedia systems research deal with the trade-off between (i) the ever-increasing content complexity, (ii) various requirements with respect to time (most importantly, latency), and (iii) quality of experience (QoE). Optimizing towards one aspect usually negatively impacts at least one of the other two aspects if not both. This situation sets the stage for our research work in the ATHENA Christian Doppler (CD) Laboratory (Adaptive Streaming over HTTP and Emerging Networked Multimedia Services; https://athena.itec.aau.at/), jointly funded by public sources and industry. In this talk, we will present selected novel approaches and research results of the first year of the ATHENA CD Lab’s operation. We will highlight HAS-related research on (i) multimedia content provisioning (machine learning for video encoding); (ii) multimedia content delivery (support of edge processing and virtualized network functions for video networking); (iii) multimedia content consumption and end-to-end aspects (player-triggered segment retransmissions to improve video playout quality); and (iv) novel QoE investigations (adaptive point cloud streaming). We will also put the work into the context of international multimedia systems research.

1. HTTP Adaptive Streaming – Quo Vadis?
Christian Timmerer, Professor at AAU, Director at CD Lab ATHENA
Hermann Hellwagner, Professor at AAU
Klagenfurt, Austria
April 20, 2022
1
Multimedia
Communications
Technical
Committee
Change log:
● MMTC DL Series’23
● PCS’21
● DDRC’21
● ISM’20
● WebMedia’20

2. The number of transistors in an integrated circuit
doubles about every two years – Moore's Law
(https://en.wikipedia.org/wiki/Moore%27s_law)
Users' bandwidth grows by 50% per year (10% less
than Moore's Law for computer speed) – Nielsen's
Law of Internet Bandwidth
(https://www.nngroup.com/articles/law-of-bandwidth/)
Video accounts for more than 65% of the global
Internet traffic – Sandvine Global Internet
Phenomena (January 2023, https://www.sandvine.com/phenomena)
2

3. Presenter
Christian Timmerer
Univ.-Prof. at Alpen-Adria-Universität Klagenfurt
Director CD Lab ATHENA
CIO | Head of Research and Standardization at Bitmovin
3
3
2003: MSc CS (Dipl.-Ing.)
2006: PhD CS (Dr.-techn.)
2012: Co-founded Bitmovin
2014: Habilitation (Priv.-Doz.) & Assoc. Prof.
2016: Dep. Director @ ITEC/AAU
2019: Director @ ATHENA
2022: Univ.-Prof. for Multimedia Systems
Web: http://timmerer.com/
Bitmovin MPEG-DASH

4. 4
4
My offices are here
Copyright: AAU/Steinthaler

5. ● Introduction
● ATHENA
○ Content Provisioning
○ Content Delivery
○ Content Consumption
○ End-to-End Aspects
○ Quality of Experience
● Conclusions: HAS – Quo Vadis?
Agenda
5

6. Motivation
Sources: * Sandvine Global Internet
Phenomena (January 2023). **
Cisco Annual Internet Report
(2018–2023) White Paper (March
2020)
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6
Video streaming is dominating today’s Internet traffic
● 2022: 65.93%; Netflix 13.74%, YouTube 10.51%, Disney+ 4.2%,
Tik Tok 3.55%, Amazon Prime 2.67%*
● Video and other applications continue to be of enormous
demand in today’s home, but there will be significant
bandwidth demands with the application requirements
of the future**

7. HTTP Adaptive Streaming 101
Adaptation logic is within the
client, not normatively specified
by a standard, subject to
research and development
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7
Client

8. Multimedia Systems Challenges and Tradeoffs
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Basic figure by Klara Nahrstedt, University of Illinois at Urbana–Champaign, IEEE MIPR 2018

9. “Application-oriented basic research” to address current and future research
and deployment challenges of HAS and emerging streaming methods
ATHENA – Adaptive Streaming over HTTP and
Emerging Networked Multimedia Services
Content Provisioning Content Delivery Content Consumption
End-to-End Aspects
● Video encoding for HAS
● Quality-aware encoding
● Learning-based encoding
● Multi-codec HAS
● Edge computing
● Information CDN/SDN⇿clients
● Netw. assistance for/by clients
● Utility evaluation
● Bitrate adaptation schemes
● Playback improvements
● Context and user awareness
● Quality of Experience (QoE) studies
● Application/transport layer enhancements
● Quality of Experience (QoE) models
● Low-latency HAS
● Learning-based HAS
https://athena.itec.aau.at/
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9
Funding:

10. Video coding for HTTP Adaptive Streaming
● Quality improvement
Per-Title Encoding (PTE) et al. for live use cases: video complexity analysis,
per-title/-scene/-shot/-segment, content-/context-aware, content-adaptive,
quality-aware encoding
● Runtime improvement
Hardware-/software-based (cloud), parallel/distributed, information reuse from
reference encodings (multi-rate/-resolution) ⇨ cf. earlier versions of this talk
● Application scenarios
Video on Demand (VoD incl. diff. flavors AVoD, SVoD), live (incl. diff. flavors),
interactive, games, video conferencing
Content Provisioning
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10

11. Problem Statement / Basic Idea for Live Streaming
● Static bitrate ladder
Pre-defined bitrate/resolution pairs for all contents
● Per-title encoding
Optimize bitrate ladder based on the content
● UHD / HFR content
Increase of spatial and temporal resolutions
● Live (is Life)[* Opus, 1085]
Perceptual-based bitrate/resolution/framerate
prediction algorithm based on content complexity
Video Complexity Analyzer (VCA)
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11
VoD
Live
* … https://www.youtube.com/watch?v=pATX-lV0VFk
Vignesh V Menon, Christian Feldmann, Hadi Amirpour, Mohammad Ghanbari, and Christian Timmerer. 2022.
VCA: video complexity analyzer. In Proceedings of the 13th ACM Multimedia Systems Conference (MMSys '22). Association
for Computing Machinery, New York, NY, USA, 259–264. https://athena.itec.aau.at/2022/04/vca-video-complexity-analyzer/

12. Spatial (E) / Temporal (h) Content Characteristics
Video Complexity Analyzer (VCA)
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*) Michael King, Zinovi Tauber, and Ze-Nian Li. 2007. A New Energy Function for Segmentation and Compression. In 2007
IEEE International Conference on Multimedia and Expo. 1647–1650. https://doi.org/10.1109/ICME.2007.4284983
*)
VCA 2.0 (seven features): the average
● luma texture energy EY
● gradient of the luma texture energy hY
● luma brightness LY
● chroma brightness (LU
and LV
)
● chroma texture energy (EU
and EV
)
https://athena.itec.aau.at/2023/02/vca-v2-0-released/

13. VCA 2.0 Latest Results
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Vignesh V Menon, Christian Feldmann, Klaus Schoeffmann, Mohammad Ghanbari, Christian Timmerer,
"Green video complexity analysis for efficient encoding in Adaptive Video Streaming," ACM Green
Multimedia Systems 2023, June 2023.
https://athena.itec.aau.at/2023/04/green-video-complexity-analysis-for-efficient-encoding-in-adaptive-video-streaming/

14. Live Per-Title Encoding Scheme
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VCA: Video Complexity Analyzer (to extract/calculate E/h metrics)
Github: https://github.com/cd-athena/VCA
Documentation: https://cd-athena.github.io/VCA/

15. Live Per-Title Encoding: Example Bitrate Ladder
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VCA: Video Complexity Analyzer (to extract/calculate E/h metrics)
Github: https://github.com/cd-athena/VCA
Documentation: https://cd-athena.github.io/VCA/

16. Live Per-Title Encoding: Example Frames (1)
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17. Live Per-Title Encoding: Example Frames (2)
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18. Results: Online Per-Title Encoding (OPTE)
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V. V. Menon, H. Amirpour, M. Ghanbari and C. Timmerer, "OPTE: Online Per-Title Encoding for Live
Video Streaming," ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP), Singapore, Singapore, 2022, pp. 1865-1869, doi: 10.1109/ICASSP43922.2022.9746745.
https://athena.itec.aau.at/2022/01/opte-online-per-title-encoding-for-live-video-streaming/

19. Perceptually-aware Per-title Encoding (PPTE)
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The minimum visual difference that
can be perceived by HVS, i.e., the
difference between two adjacent
perceptual distortion levels, refers as to
one Just Noticeable Difference (JND)
V. V. Menon, H. Amirpour, M. Ghanbari and C. Timmerer, "Perceptually-Aware Per-Title Encoding for
Adaptive Video Streaming," 2022 IEEE International Conference on Multimedia and Expo (ICME), Taipei,
Taiwan, 2022, pp. 1-6, doi: 10.1109/ICME52920.2022.9859744.
https://athena.itec.aau.at/2022/05/perceptually-aware-per-title-encoding-for-adaptive-video-streaming/

20. Results: Perceptually-aware Per-title Encoding (PPTE)
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V. V. Menon, H. Amirpour, M. Ghanbari and C. Timmerer, "Perceptually-Aware Per-Title Encoding for
Adaptive Video Streaming," 2022 IEEE International Conference on Multimedia and Expo (ICME), Taipei,
Taiwan, 2022, pp. 1-6, doi: 10.1109/ICME52920.2022.9859744.
https://athena.itec.aau.at/2022/05/perceptually-aware-per-title-encoding-for-adaptive-video-streaming/

21. ● Efficient Content-Adaptive Feature-based Shot Detection for HTTP Adaptive Streaming
(IEEE ICIP 2021)
● INCEPT: INTRA CU Depth Prediction for HEVC (IEEE MMSP 2021)
● CODA: Content-aware Frame Dropping Algorithm for High Frame-rate Video Streaming
(DCC 2022)
● OPTE: Online Per-title Encoding for Live Video Streaming (IEEE ICASSP 2022)
● Live-PSTR: Live Per-title Encoding for Ultra HD Adaptive Streaming (NAB BEITC 2022)
● Perceptually-aware Per-title Encoding for Adaptive Video Streaming (IEEE ICME 2023)
● Light-weight Video Encoding Complexity Prediction using Spatio Temporal Features (IEEE
MMSP 2023)
● ETPS: Efficient Two-pass Encoding Scheme for Adaptive Live Streaming (IEEE ICIP 2023)
● Content-adaptive Encoder Preset Prediction for Adaptive Live Streaming (PCS 2023)
● Transcoding Quality Prediction for Adaptive Video Streaming (ACM MHV 2023)
● Green video complexity analysis for efficient encoding in Adaptive Video Streaming
(ACM GMSys 2023)
VCA-based Applications
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Vignesh V Menon, Christian Feldmann, Hadi Amirpour, Mohammad Ghanbari, and Christian Timmerer. 2022.
VCA: video complexity analyzer. In Proceedings of the 13th ACM Multimedia Systems Conference (MMSys '22). Association
for Computing Machinery, New York, NY, USA, 259–264. https://athena.itec.aau.at/2022/04/vca-video-complexity-analyzer/

22. Network assistance for HTTP Adaptive Streaming
● Edge computing support (at CDN / cellular network edge)
Functions at (or, assisted by) the edge: adaptation, analytics, (pre-)fetching,
caching, transcoding, repackaging of content, request aggregation
● Server/network/CDN ↔ HAS client information exchange and collaboration
IETF ALTO, MPEG SAND, MPEG NBMP, …; SDN-DASH, SDN-HAS, SABR, ...
● Use of modern network architecture features
SW Defined Networking (SDN); Network Function Virtual. (NFV); MC-ABR
Content Delivery
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23. Different policies/metrics/resource utilization
● Prefetching based on last segment quality
○ Last segment quality (LSQ)
○ Last segment quality plus (LSQ+)
○ All segment qualities (ASQ)
● Prefetching based on a Markov Model
● Prefetching based on transrating
● Prefetching Based on machine Learning (buffer
size, link bitrate, prev. quality, prev. link bitrate;
Random Forest, Gradient Boost, AdaBoost,
Decision Trees and Extremely Randomized Trees)
● Prefetching based on super resolution
Segment Prefetching and Caching at the Edge (SPACE)
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J. Aguilar-Armijo, C. Timmerer and H. Hellwagner, "SPACE: Segment Prefetching and Caching at
the Edge for Adaptive Video Streaming," in IEEE Access, vol. 11, pp. 21783-21798, 2023
https://athena.itec.aau.at/2023/02/space-segment-prefetching-and-caching-at-the-edge-for-adaptive-video-streaming/

24. The best segment prefetching policy depends on the service provider’s
preferences and resources
● Straightforward implementation with
low resource utilization
⇨ LSQ and Markov-based
● Prioritize QoE at the expense of costs
⇨ ASQ, LSQ+ and Transrating-based
● Balance between performance and costs
⇨ ML-based
● Possible next steps: dynamically adapt;
premium clients
Segment Prefetching and Caching at the Edge (SPACE)
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25. Approach:
● Mechanism: Introduce a new server/segment selection approach at the edge of the network
● Main goal: Improve the users' QoE and network utilization
ES-HAS: An Edge- and SDN-Assisted Framework for HTTP
Adaptive Video Streaming
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R. Farahani, F. Tashtarian, A. Erfanian, C. Timmerer, M. Ghanbari, and H. Hellwagner. ”ES-HAS: An
Edge- and SDN-Assisted Framework for HTTP Adaptive Video Streaming”.
The 31st edition of the Workshop on Network and Operating System Support for Digital Audio and
Video (NOSSDAV’21), Sept. 28-Oct. 1, 2021, Istanbul, Turkey.
https://athena.itec.aau.at/2021/04/es-has-an-edge-and-sdn-assisted-framework-for-http-adaptive-video-streaming/

26. Goal
● Minimizing HAS clients’ serving time & network cost,
considering available resources
● Multi-layer architecture + centralized optimization model
executed by SDN controller ⇨ high time complexity
● Three heuristic approaches: CG, FG-I, FG-II
● Experiments on a large-scale cloud-based testbed
including 250 HAS players
⇨ improve QoE by at least 47%
⇨ decrease the streaming cost by at least 47%
⇨ enhance network utilization by at least 48%
compared to state-of-the-art
ARARAT: A Collaborative Edge-Assisted Framework for
HTTP Adaptive Video Streaming
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R. Farahani, M. Shojafar, C. Timmerer, F. Tashtarian, M. Ghanbari and H. Hellwagner, "ARARAT: A Collaborative
Edge-Assisted Framework for HTTP Adaptive Video Streaming," in IEEE Transactions on Network and
Service Management, vol. 20, no. 1, pp. 625-643, March 2023, doi: 10.1109/TNSM.2022.3210595.
https://athena.itec.aau.at/2022/09/ararat/

27. Extract some features as metadata during the encoding process ⇨ Reuse
metadata in the transcoding process at the edge
Light-weight Transcoding at the Edge (LwTE)
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A. Erfanian, H. Amirpour, F. Tashtarian, C. Timmerer and H. Hellwagner,
LwTE: Light-Weight Transcoding at the Edge," in IEEE Access, vol. 9, pp. 112276-112289, 2021
https://athena.itec.aau.at/2021/07/lwte-light-weight-transcoding-at-the-edge/
Delivery
3
4
Extract
metadata
1
2
Determine
optimal
policy 3
Optimized
download/
transcode
3
Variations:
● CD-LwTE
● LwTE-Live

28. LwTE: Binary Linear Programming (BLP) Model
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Inputs & Constraints:
●Videos/Segments Size
●Metadata Size
●Resources Cost
●Available Resources
●Probability Function
●Number of Incoming Requests
BLP Optimization Model
Outputs:
● Segments’ Serving Policy
(store/transcode/fetch)
Objective function: Minimize cost (computation,
storage, bandwidth) and serving delay

29. Performance of the proposed CD-LwTE approaches (FGH, CGH) compared with
state-of-the-art approaches in terms of (a) cost, and (b) average serving delay, for various ρ
values (the number of incoming requests at the edge server)
CD-LwTE Comparison with State of the Art
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APAC: T. X. Tran, P. Pandey, A. Hajisami, and D. Pompili, “Collaborative multibitrate video caching and processing in Mobile-Edge
Computing networks,” in 2017 13th Annual Conference on Wireless On-demand Network Systems and Services (WONS), 2017, pp. 165–172.
CoCache: T. X. Tran and D. Pompili, “Adaptive Bitrate Video Caching and Processing in Mobile-Edge Computing Networks,” IEEE
Transactions on Mobile Computing, vol. 18, no. 9, pp. 1965–1978, 2019.
PartialCache: H. Zhao, Q. Zheng, W. Zhang, B. Du, and H. Li, “A Segment-based Storage and Transcoding Trade-off Strategy for
Multi-version VoD Systems in the Cloud,” IEEE Transactions on Multimedia, vol. 19, no. 1, pp. 149–159, 2016.
A. Erfanian, H. Amirpour, F. Tashtarian, C. Timmerer and H. Hellwagner, "CD-LwTE: Cost-and Delay-aware Light-weight
Transcoding at the Edge," in IEEE Transactions on Network and Service Management, doi: 10.1109/TNSM.2022.3229744.
https://athena.itec.aau.at/2022/12/cd-lwte-cost-and-delay-aware-light-weight-transcoding-at-the-edge/
CD-LwTE
CD-LwTE

30. LwTE Findings
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30
● Stores the optimal search
decisions in the encoding
process as metadata
● Utilizes the metadata to
avoid search processes
during transcoding at the
edge
● Uses partial-transcoding
● LwTE does transcoding 80%
faster than H.265
● Up to 70% cost saving
compared to
state-of-the-art
LwTE
● Extends LwTE by relaxing
assumptions, new policy, and
serving delay to objective
● Adds new features in
metadata
● BLP model to select optimal
policy to serve requests while
minimizing cost and delay
● Reduces
transcoding time up to 97%
streaming cost up to 75%
delay up to 48%
compared to state-of-the-art
CD-LwTE
● Investigates LwTE’s
performance in live
streaming context
● MBLP model to select
optimal policy (fetching and
transcoding) to serve
requests
● Reduces
streaming cost up to 34%
bandwidth up to 45%
compared to state-of-the-art
LwTE-Live

31. Player Adaptation Logic and Quality of Experience
● Bitrate adaptation schemes
Client-based, server-based, network-assisted, hybrid, ML-based
● Application/transport layer enhancements
HTTP/2 (TCP) and HTTP/3 (QUIC), Media over QUIC (MOQ), proprietary formats (SRT, RIST, …),
WebRTC, low-latency/delay
● Client playback improvements
User-/client-aware playback, content-enhancement filters, super-resolution
● Low-latency live streaming
Use of MPEG CMAF, HTTP/1.1 Chunked Transfer Encoding (CTE), other protocol features
(e.g., HTTP/2 Push); LL-DASH/HLS; specific network functions; CDN support
● Quality of Experience
Content Consumption and End-to-End Aspects
31
31

32. Bitrate Adaptation Schemes
Bitrate
Adaptation
Schemes
Client-based
Adaptation
Bandwidth
-based
Buffer-
based
Mixed
adaptation
Proprietary
solutions
MDP-based
Server-based
Adaptation
Network-
assisted
Adaptation
Hybrid
Adaptation
SDN-based
Server and
network-
assisted
A. Bentaleb, B. Taani, A.C. Begen, C. Timmerer and R. Zimmermann, "A Survey on Bitrate Adaptation Schemes for
Streaming Media Over HTTP," IEEE Communications Surveys & Tutorials, vol. 21, no. 1, pp. 562-585, First Quarter 2019.
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Adaptive bitrate (ABR) algorithms, adaptation logics, ...

33. An HTTP/3-Based Adaptive Bitrate Approach Using Retransmission Techniques
● HTTP/3: stream multiplexing, stream priority, and request
cancellation to upgrade low-quality segments in the player
buffer while concurrently downloading the next segment
● Qualities of retrans. segments selected based on an objective
function (avg. bitrate; video instability) & throughput constraints
● Different strategies of download order for retrans. Segments
to optimize the QoE
⇨ QoE: improvement up to 33%
⇨ Video instability: decr. up to 66%
⇨ Stall duration: decr. up to 92%
⇨ Saves up to 16% of downloaded data
Days of Future Past+ (DoFP+)
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M. Nguyen, D. Lorenzi, F. Tashtarian, H. Hellwagner and C. Timmerer, "DoFP+: An HTTP/3-Based
Adaptive Bitrate Approach Using Retransmission Techniques," in IEEE Access, vol. 10, 2022
https://athena.itec.aau.at/2022/10/dofp-an-http-3-based-adaptive-bitrate-approach-using-retransmission-techniques/

34. Why?
● Mobile devices are becoming powerful, execution
time of SR-DNNs is still high
● Mobile ML frameworks are here, better QoE (at
lower cost) is required for HAS
What?
● Lightweight SR that considers the limitations of
mobile environment
● Performance on-par with SotA SR-DNNs while
running in real-time on mobile GPUs
LiDeR: Lightweight Dense Residual Network for
Video Super-Resolution on Mobile Devices
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34
E. Çetinkaya, M. Nguyen and C. Timmerer, "LiDeR: Lightweight Dense Residual Network for Video
Super-Resolution on Mobile Devices," 2022 IEEE 14th Image, Video, and Multidimensional Signal
Processing Workshop (IVMSP), Nafplio, Greece, 2022, pp. 1-5, doi: 10.1109/IVMSP54334.2022.9816346.
https://athena.itec.aau.at/2022/05/lider-lightweight-dense-residual-network-for-video-super-resolution-on-mobile-devices/

35. Extension of WISH – weighted sum model2)
● User-centric ABR based on cost
function w/ weights: throughput/data,
buffer, quality, energy, etc.
● Combined with a SR-ABR Net for SR
Super-Resolution Based Bitrate Adaptation for
HTTP Adaptive Streaming for Mobile Devices1)
35
35
1)
Minh Nguyen, Ekrem Çetinkaya, Hermann Hellwagner, and Christian Timmerer. 2022. Super-resolution based bitrate adaptation for HTTP
adaptive streaming for mobile devices. In Proceedings of the 1st Mile-High Video Conference (MHV '22). Association for Computing
Machinery, New York, NY, USA, 70–76.
https://athena.itec.aau.at/2022/02/super-resolution-based-bitrate-adaptation-for-http-adaptive-streaming-for-mobile-devices/
2) https://athena.itec.aau.at/2021/07/wish-user-centric-bitrate-adaptation-for-http-adaptive-streaming-on-mobile-devices/

36. Based on ACM MMSys’22 keynote by Ali C. Begen
LLL-CAdViSE: Live Low-Latency Cloud-based
Adaptive Video Streaming Evaluation
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36
B. Taraghi, H. Hellwagner and C. Timmerer, "LLL-CAdViSE: Live Low-Latency Cloud-Based Adaptive Video
Streaming Evaluation Framework," in IEEE Access, vol. 11, pp. 25723-25734, 2023
https://athena.itec.aau.at/2023/03/lll-cadvise-live-low-latency-cloud-based-adaptive-video-streaming-evaluation-framework/
Average latency and ITU-T P.1203 MOS of MPEG-DASH (dash.js) and HLS (hls.js) using
three different ABR algorithms (default, L2A-LL, LoLP) with a given target latency of 3s.
https://github.com/cd-athena/LLL-CAdViSE

37. Fixed one-size-fits-all bitrate ladder problem
● Content complexities, heterogeneous network
conditions, viewer device resolutions and
locations ⇨ impacts QoE
● LALISA: Dynamic bitrate ladder optimization for
live HAS ⇨ improves QoE and reduces encoding,
storage, and bandwidth costs
● Deployed on top of existing ABRs and closes the
gap between selected bitrate and desired bitrate
● LP Agent to extract desired bitrate; Mixed Integer
Linear Programming (MILP) model at LA server;
LO Agent to predict VMAF of next segment to be
encoded
LALISA: Adaptive Bitrate Ladder Optimization in
HTTP-based Adaptive Live Streaming
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F. Tashtarian, A. Bentaleb, H. Amirpour, B. Taraghi, C. Timmerer, H. Hellwagner, R. Zimmermann, LALISA:
Adaptive Bitrate Ladder Optimization in HTTP-based Adaptive Live Streaming, IEEE/IFIP Network
Operations and Management Symposium (NOMS), Miami, FL, USA, May 2023.
https://athena.itec.aau.at/2022/12/lalisa-adaptive-bitrate-ladder-optimization-in-http-based-adaptive-live-streaming/
(seg#, selected br, desired br)
MILP model to
determine optimal
bitrate ladder

38. Trace-driven testbed
LALISA reduced bandwidth consumption,
encoding computation demand, and bitrate
ladder size by 18.2%, 24.4%, and 33.3%
respectively, while playing at good quality
(VMAF) with high viewer experience during live
video sessions
LALISA Results
38
38
Average % (over all segment durations) of improvement and degradation of LALISA vs. BL-9.

39. Quality of Experience (QoE) ...
● “... is the degree of delight or annoyance of the user of an application or service.
It results from the fulfillment of his or her expectations with respect to the utility and / or
enjoyment of the application or service in the light of the user’s personality and current state.”1)
● … can be easily extended to various domains, e.g., immersive media experiences.2)
39
39
1)
P. Le Callet, S. Möller, A. Perkis, et al. QUALINET White Paper on
Definitions of Quality of Experience. European Network on Quality of
Experience in Multimedia Systems and Services (COST Action IC 1003). 2012.
2)
A. Perkis, C. Timmerer, et al. 2020. QUALINET White Paper on Definitions
of Immersive Media Experience (IMEx). arXiv:2007.07032 [cs.MM]
3)
Jeroen van der Hooft, Tim Wauters, Filip De Turck, Christian Timmerer, and
Hermann Hellwagner. “Towards 6DoF HTTP Adaptive Streaming Through
Point Cloud Compression”.
27th ACM Int’l. Conf. on Multimedia (MM'19). Oct. 2019.
4)
J. van der Hooft, M. T. Vega, C. Timmerer, A. C. Begen, F. De Turck and R.
Schatz, "Objective and Subjective QoE Evaluation for Adaptive Point
Cloud Streaming," 2020 Twelfth International Conference on Quality of
Multimedia Experience (QoMEX), Athlone, Ireland, 2020.
3) 4)

40. Immersive Video Delivery:
From Omnidirectional Video to Holography
4
40
J. v. d. Hooft, H. Amirpour, M. Torres Vega, Y. Sanchez, R. Schatz; T. Schierl, C. Timmerer, "A Tutorial on
Immersive Video Delivery: From Omnidirectional Video to Holography," in IEEE Communications
Surveys & Tutorials, 2023 (early access) doi: 10.1109/COMST.2023.3263252.
● 3DoF Omnidirectional Video
● 6DoF Volumetric Video
● 6DoF Imagery Video

41. Content Delivery
● Edge computing support
● CDNs, SDN, NVF, … ⇿ clients
(CMCD/CMSD)
● Live low-latency streaming
HTTP Adaptive Streaming – Quo Vadis?
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Content Consumption & End-to-End
● Client-side content improvement, SR
● Machine learning
● Energy awareness & optimizations
https://athena.itec.aau.at/
Content Provisioning
● Content and context awareness
● Multi-codec (AVC, HEVC, VVC, VP9, AV1)
● Machine learning (MPEG-/JPEG-AI)
Quality of Experience
● Immersive content (3DoF, 6DoF, live)
● User-/client-aware playback
● Better/new QoE models and analytics

42. ATHENA team
Hadi Amirpour, Jesús Aguilar Armijo, Emanuele Artioli, Ekrem Çetinkaya, Reza Ebrahimi, Alireza
Erfanian, Reza Farahani, Mohammad Ghanbari, Selina Zoë Haack, Andreas Kogler, Gregor
Lammer, David Langmeier, Vignesh V Menon, Minh Nguyen, Engin Orhan, Lingfeng Qu,
Jameson Steiner, Babak Taraghi, Farzad Tashtarian; Hermann Hellwagner, Christian Timmerer
(Inter-)National Collaborators
● Prof. Ali C. Begen, Ozyegin University, Turkey
● Prof. Filip De Turck, Ghent University – imec, Belgium
● Dr. Jeroen van der Hooft, Ghent University – imec, Belgium
● Dr. Maria Torres Vega, Ghent University – imec, Belgium
● Dr. Raimund Schatz, AIT Austrian Institute of Technology, Austria
● Prof. Roger Zimmermann, NUS, Singapore
● Dr. Abdelhak Bentaleb, Concordia University, Canada
● Prof. Christine Guillemot, INRIA, Rennes, France
Acknowledgments
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https://athena.itec.aau.at/

43. Thank you for your attention
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