Ali C. Begen

Industry Fellow: Ali C. Begen

Two things are infinite: the universe and problems in the streaming space; and I’m not sure about the streaming space. The SVTA has achieved so much already and will continue doing so. It is growing and its impact will be growing, too. I am delighted to be recognized as an SVTA Industry Fellow. I would say that years of research, development and teamwork have paid off. My gratitude, special thanks and congratulations go to all my colleagues, collaborators and students.

About Ali C.

Ali C. Begen is currently a computer science professor at Ozyegin University and a technical consultant in Comcast’s Advanced Technology and Standards Group. Previously, he was a research and development engineer at Cisco. Begen received his PhD in electrical and computer engineering from Georgia Tech in 2006. To date, he received several academic and industry awards (including an Emmy® Award for Technology and Engineering), and was granted 30+ US patents. In 2020 and 2021, he was listed among the world’s most influential scientists in the subfield of networking and telecommunications. More details are at

Industry Accomplishments

Ali has numerous industry awards and accolades related to his work in the streaming industry:
  • 2022 – ACM SIGMM Test of Time Paper Award in the category of multimedia systems-networking (link)
  • 2022 – Gold medalist, Istanbul International Inventions Fair
  • 2022 – Emmy® Award for Technology and Engineering for standardization of HTTP encapsulated protocols (innovator statue)
  • 2021 – Best Demo and Industry Paper Award, ACM MMSys
  • 2021 – DASH-IF Excellence in DASH Award (1st and 2nd places)
  • 2021 – DASH-IF dash.js Contributor Award (3rd place)
  • 2021 – Winner in YouTube’s grand challenge on “Detecting Cheapfakes”
  • 2020 – Listed among the world’s most influential scientists in the subfield of networking and telecommunications (Re-listed in 2021)
  • 2020 – Runner-up in Twitch’s grand challenge on “Adaptation Algorithms for Near-second Latency”
  • 2020 – Best-paper Award, IEEE QoMEX
  • 2019 – Royal Society Newton Fellowship
  • 2019 – ACM SIGMM Award for Outstanding PhD Thesis (Received by Abdelhak Bentaleb under my co-supervision)
  • 2019 – DASH-IF Excellence in DASH Award (1st place) (linklinklink)
  • 2018 – Winner in DASH-IF’s grand challenge on “Dynamic Adaptive Streaming over HTTP” (linklink)
  • 2018 – DASH-IF Excellence in DASH Award (1st place) (linklinklink)
  • 2018 – Best Student-paper Award, ACM MMSys
  • 2016 – Distinguished Lecturer, IEEE Communications Society (Re-elected for another two-year term in 2018)
  • 2015 – Best-paper Finalist, The Best of IET and IBC
  • 2012 – Best-paper Award, Packet Video
  • 2009 – Cisco Pioneer Award (highest technical achievement award in Cisco), in the core technology category for developing the VQE solution
  • 2008 – Most-cited Paper Award, EURASIP Signal Process.: Image Commun. Journal
  • 2004 – Qualstar Hall of Fame Award, for contributions to the standards development, Qualcomm Incorporated
Ali has been awarded numerous patents related to work in the streaming industry:
  • US11166072: Metrics and messages to improve experience for 360-degree adaptive streaming
  • US11076187: Systems and methods for performing quality based streaming
  • US10623785: Streaming manifest quality control
  • US10298985: Systems and methods for performing quality based streaming
  • US10142387: Distributed coordination of network elements for packet encapsulation
  • US10070348: Hypertext transfer protocol support over hybrid access
  • US9935991: Pipelining get requests in adaptive streaming
  • US9866605: Streaming manifest quality control
  • US9832515: DTS/PTS backward extrapolation for stream transition events
  • US9591098: System and method to reduce stream start-up delay for adaptive streaming
  • US9426335: Preserving synchronized playout of auxiliary audio transmission
  • US9402114: System and method for providing randomization in adaptive bitrate streaming environments
  • US9386308: Quality optimization with buffer and horizon constraints in adaptive streaming
  • US9338209: Use of metadata for aiding adaptive streaming clients
  • US9312989: Importance-based FEC-aware error-repair scheduling
  • US9306994: Stabilization of adaptive streaming video clients through rate limiting
  • US9288136: Method and apparatus for in-band channel change for multicast data
  • US9215182: Enhancing performance of rapid channel changes and other playback positioning changes in adaptive streaming
  • US9049481: Fine-tuning the time for leaving/joining a multicast session during channel changes
  • US8953452: Enhancing performance of rapid channel changes and other playback positioning changes in adaptive streaming
  • US8924580: Constant-quality rate-adaptive streaming
  • US8843656: System and method for preventing overestimation of available bandwidth in adaptive bitrate streaming clients
  • US8837586: Bandwidth-friendly representation switching in adaptive streaming
  • US8819714: Ratings and quality measurements for digital broadcast viewers
  • US8812621: Reducing fetching load on cache servers in adaptive streaming
  • US8787153: Forward error correction based data recovery with path diversity
  • US8731000: Decoding earlier frames with DTS/PTS backward extrapolation
  • US8655156: Auxiliary audio transmission for preserving synchronized playout with paced-down video
  • US8655143: Supplementary buffer construction in real-time applications without increasing channel change delay
  • US8619602: Capacity/available bandwidth estimation with packet dispersion
  • US8301982: RTP-based loss recovery and quality monitoring for non-IP and raw-IP MPEG transport flows
  • US8300667: Buffer expansion and contraction over successive intervals for network devices
  • US8248942: Monitoring of real-time transport protocol (RTP) packet flow along RTP path
  • US7830908: Systems and methods of reducing delay in decoding